case study on green logistics

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Report | 2023

The green logistics playbook, sustainable supply chain best practices for g20 leaders.

The logistics sector serves as a crucial catalyst for economic activity, fulfilling the requirements of businesses and consumers globally. The sector is projected to undergo exponential growth in this decade, from US$8.9 trillion in 2023 to US$18.2 trillion by 2030. While the logistics sector is essential for trade and supply chains, it’s also a major source of carbon emissions and air pollution. The sector is responsible for a tenth of global emissions, underscoring the need for adopting sustainable strategies to reduce emissions.

This urgency is particularly pronounced within the Group of Twenty (G20), which consists of the EU and 19 individual countries (Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, South Korea, Mexico, Russia, Saudi Arabia, South Africa, Turkey, the UK, and the United States). Collectively, the G20 nations are home to two-thirds of the world’s population and account for 85 percent of the global Gross Domestic Product (GDP). The G20 nations are also responsible for nearly 80 percent of global greenhouse gas emissions, a significant portion of which comes from the logistics sector.

The Green Logistics Playbook provides an actionable toolkit for G20 leaders, offering concrete solutions and successful case studies on sustainable logistics practices across G20 nations that can address climate change, enhance livelihoods, improve public health, and foster economic growth. The solutions presented in the report are divided across four key strategies:

• Logistics operations: Driving robust innovation, research and development, and on-the-ground deployment of sustainable logistics measures.
• Policy drivers: Incentivizing the adoption of efficient logistics practices and conveying potent market signals to prioritize sustainability within the sector.
• Infrastructure development : Strategically planning and deploying a network of physical facilities for storing and transporting goods.
• Financial investments : Facilitating public–private partnerships to mobilize finance for infrastructure and projects geared toward sustainability.

The report outlines system-level changes that the G20, as a global collaborative forum, can achieve by partnering with the participating nations.

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Green Logistics: Meaning, Tips, and Challenges

The transportation industry stands at a pivotal crossroads. With increasing concerns about environmental issues, green logistics emerged as a potential solution to tackle rising carbon emissions, waste management, and other challenges associated with conventional logistics operations.

This article offers insights into green logistics and its importance in fostering sustainable solutions in the industry and how it impacts the supply chain. Understand how companies, influenced by customer demand and eco-conscious values, prioritize sustainability for a brighter, cleaner future.

How Green Logistics Works in the Supply Chain

Green logistics involves implementing sustainable practices in logistics operations to reduce the environmental impact. By incorporating green logistics strategies, businesses can contribute to environmental sustainability and find ways to save money and boost their brand image. 

These strategies focus on reducing waste, fuel consumption, greenhouse gas emissions, and energy consumption while ensuring efficient supply chain management. A shining example is UPS, which adopted route optimization for its delivery vehicles, resulting in fewer shipments and lower fuel consumption. Such successes show how a shift toward sustainable logistics benefits the planet and the company’s bottom line.

As a logistics company explores sustainable options, harnessing new technological advances plays a significant role. Integrating AI technology offers predictive analytics that utilizes the optimization of supply routes and inventory management. 

Similarly, blockchain technology allows transparent and efficient supply chain tracking and builds confidence in stakeholders by maintaining environmentally friendly practices. Thus, leveraging these technologies expedites the shift toward more sustainable logistics operations.

Regenerating a Supply Chain by Reducing Greenhouse Gas Emissions

The global concern about rising greenhouse gas emissions pushed logistics providers to consider eco-friendly practices.

Challenges: High upfront investment in sustainable materials and alternative fuels, resistance to change within traditional supply chains, and the pressure of competitive advantage in logistics processes can create barriers.

Benefits: Reduced carbon dioxide emissions, improved brand reputation, and potential cost savings in the long run.

Deterrent or Duty?: While some see it as a hefty investment, the long-term advantages and the growing customer demand for environmentally conscious businesses make it an essential duty.

The emergence of digital platforms and software applications aids in tracking a company’s carbon footprint in real time and offers invaluable insights into logistics processes. Such tools enable businesses to develop action plans, target specific areas, and track the progress of their carbon footprint.

Importance of Collecting CO2 Data

Accurate collection and analysis of CO2 data is paramount in the logistics industry.

Challenges: The accuracy of tools to measure carbon footprint and other metrics across the industry and costs associated with data collection systems.

Benefits: Understanding a company’s environmental footprint allows for targeted sustainability efforts and showcases transparency in the supply chain to customers.

Deterrent or Duty?: With increasing regulations and growing awareness of climate change, CO2 data collection is becoming a requisite for businesses.

With the adoption of IoT (Internet of Things) devices, companies have an unprecedented level of granular data. Combined with analytics, this data provides actionable insights for better fleet management, load optimization, and route planning, ensuring the least environmental impact possible.

Carbon Offsetting Tactics Make a Difference

Companies are considering carbon offsetting as essential to their green logistics strategy.

Challenges: Identifying genuine offset projects and understanding the real-world impact of these projects and the potential cost of investing in these initiatives.

Benefits: Directly contributes to reducing the impact of carbon emissions on the environment and enhances brand image.

Deterrent or Duty?: As the detrimental effects of carbon emissions become more evident, carbon offsetting transforms from a good-to-have initiative to a necessary business practice.

Collaborations with environmental NGOs and agencies can offer insights and support for companies venturing into carbon offsetting. Such collaborations also provide a credible front to the company’s efforts, as these agencies are often recognized for their work in environmental conservation.

Alternate Fuel Choices and Environmental Impact

Alternative fuels, such as biofuels and hydrogen, present opportunities for the logistics sector.

Challenges: Infrastructure for alternative fuel vehicles, initial investment costs, and the time to see a return on investment.

Benefits: Significant reductions in greenhouse gas emissions, lower transportation costs, and a shift away from reliance on fossil fuels.

Deterrent or Duty?: As fossil fuel reserves deplete and their impact on the environment becomes clearer, turning to alternative fuels isn’t just an option but a pressing need.

In the realm of biofuels, advancements in algae-based fuels present exciting possibilities. 

Algae-derived biofuels are more sustainable, don’t compete with food sources, and can be grown in diverse environments. Thus, their integration into the logistics sector could be transformative.

Electric Delivery Methods

The advent of electric vehicles (EVs) is reshaping the logistics industry.

Challenges: High initial costs, limited battery range, and a lack of charging infrastructure affect the supply chain.

Benefits: Drastic reduction in carbon emissions, energy savings, and reduced noise pollution.

Deterrent or Duty?: The push towards EVs is undeniable. With the dual benefits of environmental conservation and potential long-term savings, electric delivery methods are replacing the industry standard.

Innovation in battery technology, such as solid-state batteries, promises longer battery life and faster charging times. Coupled with solar charging stations and renewable energy sources, the future of electric transportation in the logistics sector seems brighter than ever.

Green Logistics and Reducing Carbon Footprints

In an era dominated by global discussions around climate change, the shipping industry is under intense scrutiny. Transport vessels emit significant greenhouse gasses and impact our environment. Consequently, the ripple effect of this pollution is extensive. 

It negatively affects the environment and a company’s brand image, customer relationships, and operational costs.

Adopting green logistics becomes an environmental imperative and a strategic move for businesses seeking long-term viability and growth.

Create Customer Awareness

Engaging customers in the sustainability journey is paramount. By raising awareness about a company’s eco-logistics efforts, businesses can fortify trust and loyalty among existing customers while attracting a new, environmentally-conscious clientele. Transparent communication about sustainability can reinvigorate old relations and set the foundation for future partnerships.

Leveraging digital platforms and social media can amplify the message. Companies can foster a community of eco-conscious consumers and stakeholders by sharing behind-the-scenes glimpses of sustainable initiatives, stories of successful green transitions, or even educational content about the environment.

Regenerating Route Maps and Capturing Energy Savings

Switching to efficient route maps and delivery methods sets companies on a new growth trajectory. Through optimized route planning, companies minimize fuel consumption and thereby reduce carbon emissions. Furthermore, by exploring alternative energy-saving delivery methods, businesses can diminish their environmental footprint while capturing significant energy and cost savings.

Reduce, Recycle, and Promote Green Logistics

Embracing the reduction, recycling, and reuse principles can profoundly transform logistics practices. By reducing waste, especially in packaging and recycling materials, companies significantly diminish their environmental footprint. Promoting alternative delivery methods and integrating them into the core logistics network is the future of sustainable and eco-friendly operations.

Challenges Facing Green Logistics Industry

Transitioning to green logistics has its challenges. Companies depend on high upfront investments, struggle with resistance within their operations, and battle a labyrinth of regulations and standards that are challenging to navigate.

Cost of Going Green

Embracing sustainable practices demands a generous upfront investment. From overhauling fleets with eco-friendly vehicles to implementing sophisticated tracking systems for carbon emissions, the initial costs are daunting and make businesses hesitant to convert to green solutions.

Partnering With Green Solution Companies

Building partnerships with green logistics providers can be a catalyst for sustainable transformation. These collaborations offer access to cutting-edge technologies, shared expertise, and best practices. However, fostering such relationships requires trustworthy alignment regarding values, goals, and long-term vision.

Benefits of Switching

Opting for green logistics solutions transcends environmental benefits. In the long run, companies will harvest significant cost savings through energy efficiency and waste reduction. Additionally, adopting eco-friendly practices enhances brand reputation within a growing segment of environmentally-conscious consumers and positions the business as a forward-thinking industry leader.

Top Tips for Going to Green Logistics

While the transition to green logistics encompasses many challenges, exciting, innovative tools and strategies continually emerge to facilitate this shift. By harnessing these resources, companies can mitigate environmental impact while optimizing their operations.

Alternative Green Shipping Materials

Sustainable packaging materials like biodegradable plastics or reused cardboard significantly reduce waste. Websites like the Sustainable Packaging Coalition offer insights and guidelines on choosing eco-friendly alternatives for shipping needs.

Impact of Shipping Full-Loads and Space-Saving Tips

Maximizing load capacity reduces the trip cost by reducing fuel consumption and emissions. Resources like the National Industrial Transportation League provide guidelines on efficient load planning and space optimization.

Reducing Customer Returns Effectively

Implementing robust quality checks and accurate product descriptions can minimize return rates. Reverse logistics strategies and best practices reduce and manage returns sustainably.

Strategies for No-Fail Deliveries

Leveraging technology for real-time tracking, route optimization, and predictive analytics can ensure timely and accurate deliveries. Many organizations offer initiative green logistics tools and strategies to enhance delivery accuracy and efficiency.

Re-Thinking Carbon Emissions

Considering carbon offset programs or investing in alternative fuel vehicles plays a prominent role in diminishing carbon footprints. The Carbon Fund is an excellent resource for companies seeking to offset emissions and contribute to global sustainability projects.

Management Role in Green Logistics

Leadership’s commitment is pivotal for successful implementation. By setting clear sustainability goals, managers can drive organizational change. The Environmental Leader offers case studies and insights on how top executives can champion green initiatives in the logistics sector.

Why Green Logistics Companies Matter

The rise of green logistics companies symbolizes a revolutionary shift in the logistics industry, emphasizing sustainability and eco-friendly practices. Eco-conscious companies like DHL, with its GoGreen program, and UPS, with its alternative fuel fleet, project the industry’s evolution towards reducing carbon emissions and championing environmental stewardship.

Diving into some of the commonly asked questions about green logistics.

What is meant by green logistics?

Green logistics refers to optimizing logistics and supply chain operations in an eco-friendly manner, minimizing environmental impact.

What are examples of green logistics?

Examples include using electric delivery vehicles, optimizing routes to save fuel, and utilizing biodegradable packaging materials.

Is Green Logistics a real company?

No, Green Logistics isn’t a specific company. It’s a term referring to sustainable and eco-friendly logistics practices.

Key Takeaways on Implementing Green Logistics

Embracing green logistics and other efficient logistics processes isn’t just a trend; it’s necessary for the environment and business longevity.

Implementing green logistics strategies allows companies to reduce their carbon footprint, optimize operations, and resonate with environmentally conscious consumers. As the logistics sector continues to evolve, prioritizing sustainability will be paramount.

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Green Logistics: What It Is and Why It Matters

Green logistics includes any business practice that minimizes the environmental impact of the logistics network and delivery. Sustainable logistics or green logistics secure a strong bottom line without sacrificing customer satisfaction, or the well-being of the planet. Intelligent businesses are rushing to understand and embrace sustainable logistics management, supported by powerful technologies such as artificial intelligence, machine learning, and advanced analytics.

As enterprises make the shift toward greener logistics, they realize benefits across the business, including improved profitability and good corporate citizenship. But a primary driver is customer demand. As customers (both businesses and consumers) see the real-world results of climate change on newsfeeds and streaming channels daily, they are quickly shifting loyalties to companies that demonstrate significant, permanent steps toward a sustainable future. Customers (and shareholders) advocate for a circular supply chain that incorporates reverse logistics, and are not content with or influenced by “greenwashing.”

Reverse logistics and circular supply chains

Traditionally, supply chains have been linear and unidirectional: raw materials are processed into products and shipped to customers, who then dispose of them. Today, this flow is being disrupted with two practices – reverse logistics and circular supply chains – that add bottom-line value to supply chains while reducing environmental impact .

• Reverse logistics: As the name implies, reverse logistics refers to processes related to the return of items and goods traveling backward through the supply chain. This can include repairs and maintenance, returns of defective items, reuse of packaging, or recycling and reclamation of end-of-life products. For businesses, today’s reverse logistics challenges most often come in the form of customer returns . Online purchases contribute to a much higher rate of customer returns than in-store purchases. This issue is further exacerbated by the business model of “subscription box” brands (typically fashion), which are based entirely on the concept of customers selecting from a wide assortment of delivered goods and returning whatever they decide not to keep. In fact, as this trend progresses, estimates are for the global amount of e-commerce returns to exceed one trillion dollars over the coming decade. Furthermore, transporting returned inventory creates more than 15 million metric tons of CO2 in the U.S. alone each year.
• Circular supply chains : A circular supply chain is a loop in which organizations reclaim as much as possible, from raw materials to finished products. In its simplest form, this means realizing value from end-of-life products, often by recycling their primary components. For example, plastics can be shredded and repurposed – even into the very shipping pallets that are used to move goods. And as the world’s metal supplies diminish, there is meaningful value to be had in extracting gold, copper, and other recyclable commodities from otherwise discarded items.

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Green transport and the growing use of commercial EVs

At the height of the COVID pandemic, online shopping rose to an all-time high with parcel volume in the US alone, growing 37% from 2019 to 2020, reaching 55 million deliveries each day . The Amazon Effect put further strain on logistics operations with consumers expecting deliveries within a day – and sometimes even within a few hours. This means goods can no longer be warehoused in a single location and distributed nationally. To achieve such aggressive delivery speeds, items must be stored in local distribution centers and then rushed to consumers in smaller batches. This calls for larger fleets of smaller vehicles.

And as the pandemic shifts and restrictions lift, these trends show no sign of slowing. According to the World Economic Forum , we should expect demand for urban last-mile delivery to grow as much as 78% by 2030, and add up to 36% more delivery vehicles in the world’s largest 100 cities.

To meet these changing delivery demands, businesses are rapidly shifting to EV fleets. At less than half the cost per mile for electricity as for gas or diesel, and without any need for tune-ups or oil changes, EV fleets have lower operating costs and less downtime . For businesses, another advantage of EVs is the ease with which they can be integrated into a greater cloud-connected supply chain network. This means that businesses can use AI-powered technologies to analyze both past and real-time operational data – delivering powerful (and actionable) insights into ways to save money, lower fuel consumption, and streamline their operations overall.

The capacity and size of modern EVs is also becoming increasingly diverse. Today, we are seeing a rise in not only light commercial vehicles (LCVs) like cargo vans but also a growing range of electric semi-trucks and long-haul transport vehicles.

And when it comes to greener transport, let’s not forget that some 80-90% of the world’s goods are transported by sea. Each year, container ships spew about 1 billion metric tons of carbon dioxide into the air — about three percent of all greenhouse gas emissions — and tons of toxic waste left in the oceans. Recognizing this, in September 2021, the International Maritime Organization (IMO), representing 150 industry leaders, set a decarbonization goal to reduce emissions by 50% by 2050, compared to 2008 levels.

Danish company Maersk (whose ships emitted 33 million tons of CO 2 in 2020) ordered eight new vessels that run on carbon-neutral methanol to help meet that ambitious goal. Shipping companies in Japan and Norway are also bringing significant innovation to the marine cargo sector, unveiling fully electric tanker ships and even the world’s first autonomous electric cargo carrier which (using radar, infrared, and automotive integrated solutions cameras) can be operated and moored entirely via remote control.

A connected logistics system helps improve profitability and brand perceptions while reducing environmental impact.

Alternative distribution networks and green logistics solutions

Of course, making the switch to EVs and alternative fuels is probably the most significant change when it comes to greener logistics. However, as McKinsey’s Bernd Heid points out “ in an 'ecosystem scenario' in which both public and private players work together effectively, delivery emissions and congestion could be reduced by 30%...when compared to a 'do nothing' scenario ”. To achieve maximum cost efficiency, faster delivery speeds, and meaningful reductions in emissions and waste, businesses will need to consider more collaborative logistics methods, and a more sophisticated array of optimizations.

A few additional optimization strategies include:

• Load pooling: A growing trend in optimized supply chain management sees similar (even competitive) companies working together to pool their warehouse and logistics resources. At first glance, this can seem like a challenging concept but fortunately, cloud-connected logistics management technologies are helping businesses to collaborate and cooperate with maximum visibility and control.
• Unbranded parcel lockers : Amazon pioneered the idea of neighborhood parcel lockers to shorten routes and speed up delivery. This is highly effective but has tended to shut out the competition. Unbranded community parcel lockers function similarly to the existing Amazon locker networks, but are accessible to a much broader range of delivery providers. By making this resource more widely available, the major logistics providers can work together to save time and money – and improve consumer choice.
• Automated load optimization : This refers to coordinating items (held in warehouses and distribution centers) with similar delivery ETAs and destinations. With today’s volumes, it’s essentially impossible to achieve this via manual efforts but smart supply chain solutions can identify and automate vehicle loading, to help eliminate the costly practice of sending delivery vans out with only half a load.
• Night-time delivery : The more time vehicles spend on the road, the greater the amount of fuel and energy used. Especially in urban areas, making deliveries at night can reduce road-time and congestion by up to 15% . Furthermore, with EVs being quieter, there is less risk of adding to night-time noise pollution.
• On-demand micro-mobility networks : Micro-mobility refers to small – often two-wheeled – vehicles like electric scooters and e-bikes. Modern logistics technologies now give drivers easy access to cloud-connected apps. This means connectivity with the home base (dispatch) and the customer (delivery ETAs) in real time. By leveraging an on-demand network of independent drivers (not exclusively employed by any one business), companies are reaping significant savings in both fuel usage, and the cost of maintaining standing fleets.
• Dynamic route allocation : In urban settings, cloud-connected route allocation tools can assess traffic, parking, even construction or other delays. In rural areas, other factors may be more relevant such as road and weather conditions, or distance from EV charging stations. By incorporating this kind of intel into real-time route planning, companies can increase delivery speed and minimize fuel consumption.
• Drones and automated vehicles : It’s visually compelling to think of drones crossing the skies and dropping packages like mechanized storks, or unmanned robots rolling down city sidewalks, laden with parcels. In reality though, we are still a few years away from fully automated logistics networks. But innovation is fast in this sector and digital automation is at the fore of many green solutions – so watch this space…

Sustainable Logistics on the Move

How technology is paving the way to greener, more sustainable logistics.

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Advantages of green logistics

The advantages of green logistics accrue to the company, its suppliers and partners, its customers, and every member of society. Here are just a few:

• Improved long-term profitability : From first to last-mile delivery, green logistics cut waste, cost, and carbon emissions. Although realizing the advantages of green logistics requires an upfront investment, the downstream benefit outweighs the cost. A recent study found “evidence that High Sustainability companies significantly outperform their counterparts over the long-term, both in terms of stock market as well as accounting performance.” The bottom line? Green business equals good business.
• New or enhanced partnerships : When businesses use sustainable supply chains and green logistics, they’re not only more attractive to customers but to corporate partners as well. A recent study from HBR found that the largest global multinationals are using the United Nations Global Compact or the Carbon Disclosure Project’s (CDP’s) Supply Chain Program to assess their suppliers’ levels of sustainability and environmental impact. Suppliers, in turn, are eager to partner with the largest brands and are making investments to try to reduce their carbon footprints.
• Happier, loyal customers : Customers – both retail and business – demand fast delivery and the flexibility to make easy returns. They want to know where their products came from, whether they’re sustainably sourced and transported, and where they are in their journey – in real time. Companies that offer these insights and transparency gain new customers and earn long-term loyalty among existing ones.
• Better corporate responsibility reputation: Large companies are increasingly called to the mat to answer for their contribution to global warming, which is considered a social justice issue. Publicly leveraging the advantages of green logistics will help companies win in the court of public opinion. Smart companies are scrutinizing their environmental footprint locally, as well as globally. Those that aren’t willing to change, especially in moving away from fossil fuels, risk their reputation and are at a competitive disadvantage.
• Easier recruitment: In the tightest job market in decades, every company advantage matters. An organization focused on green logistics is more attractive to young professionals who desire to work for a company that embodies their values.

Green logistics strategies

Organizations that combine a cloud-based smart supply chain with mobile technologies get a birds-eye view of their entire logistics process, from manufacturing to delivery to returns. But green logistics isn’t achieved in isolation. Successful implementation requires planning and the inclusion of all the various stakeholders. Below are a few suggested steps:

• Collaborate with suppliers, vendors, third- and fourth-party logistics (3PL and 4PL) partners, and experienced advisors to develop environmentally-friendly procurement protocols and eco-friendly shipping options.
• Use AI-powered technologies like supply chain control towers to integrate carbon footprint analysis into all stages of the business.
• Engage with corporate networks to share logistics resources and data-driven insights. Even brands that are typically competitive can become partners for a shared purpose.
• Strategize and right-size your fleet. Build in the ability to handle fluctuating demand with elastic logistic networks so that trucks aren’t sitting idle. For last-mile delivery, consider adding micro-mobility vehicles, such as e-bikes or drones.
• Educate customers on the impact of fast delivery speeds versus more sustainable choices. Amazon, for example, encourages customers to pick an “Amazon Day” that groups packages into fewer shipments, which saves money on packaging and transportation.

Green logistics and the future of distribution networks

Robust, AI-powered, cloud-based logistics solutions are at the core of the supply chains of the future – helping businesses to consolidate loads, automate dispatch and tracking, optimize routes, determine when and where to charge batteries, calculate ETAs, monitor vehicle maintenance, and more. Data modeling and simulations can test routes and fleet capacities, and integrated technologies can help incorporate and analyze supply chain and delivery data across the entire value chain. Every step toward the smoother and faster movement and delivery of goods, is a win/win, making customers happier and more engaged, and helping businesses to improve both their sustainability profiles and their bottom lines.

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Leading Sustainable Organizations

In 2011, the Environmental Defense Fund (EDF) approached Dr. Edgar Blanco, research director of the MIT Center for Transportation & Logistics (MIT CTL), to develop a series of case studies designed to help companies embrace “carbon-efficient” strategies in logistics operations. The case studies would provide examples of financially viable logistics strategies that reduce greenhouse gas (GHG) emissions (primarily CO 2 ) by reducing the amount of fuel and energy consumed to move products along the supply chain.

Three companies from different sectors — Boise, Inc., Ocean Spray and Caterpillar — were selected to participate in the project. The enterprises were at various stages of implementing logistics initiatives and were unsure of the GHG impact of these projects. In addition to providing technical details on how to assess the environmental impact of logistics initiatives, the case studies showed that collaboration is often at the center of achieving the expected financial and environmental benefits.

Image courtesy of Flickr user johnelmer .

Companies worldwide have been working hard to reduce their carbon footprints. And one of the biggest challenges in that effort is the process of moving goods from point A to point B — the collection of transport and storage activities commonly referred to as “logistics.”

The ships, trucks, trains, airplanes, shipping containers, and warehouses that the logistics function uses to deliver products and services both locally and globally account for almost 6% of the GHG emissions generated by human activity. The EPA 1 2 estimates that freight movements consume over 35 billion gallons of diesel fuel each year in the U.S. Burning this fuel produces more than 350 million metric tons of CO 2 , which is over 20% of all the GHG emissions generated by transportation-related fuel combustion. These emissions are neither declining nor stable — they’re growing, and fast.

There are known logistics strategies that can both significantly reduce CO 2 emissions 3 and produce cost savings.

About the Authors

Dr. Edgar Blanco is a Research Director at the MIT Center for Transportation & Logistics. Ken Cottrill is Research Marketing and Development Lead at the MIT Center for Transportation & Logistics.

1. EPA 430-R-13-001, U.S. EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2011, April 2013

2. http://www.epa.gov/smartway

3. In the U.S., CO₂ represents over 80% of GHG emissions and over 90% of logistics related emissions. Thus, it is the main GHG gas under analysis.

4. The Boise organization described in this case study, refers to Boise Cascade LLC, the paper and forest products. It should not be confused with Boise Cascade Corporation that is now OfficeMax Incorporated.

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• DOI: 10.1016/J.IJPE.2010.04.041
• Corpus ID: 153396150

Green logistics at Eroski: A case study

• S. Ubeda , F. Arcelus , J. Faulin
• Published 1 May 2011
• Environmental Science, Business
• International Journal of Production Economics

329 Citations

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A Systematic Literature Review of Green and Sustainable Logistics: Bibliometric Analysis, Research Trend and Knowledge Taxonomy

1 College of Defense Engineering, Army Engineering University of PLA, Nanjing 210042, China; moc.361@1080iurner (R.R.); moc.361.piv@lz-nehC (Z.C.)

2 College of Civil Engineering, Nanjing Tech University, Nanjing 211816, China; moc.361@9708obnus

Jianjun Dong

3 Research Institute for National Defense Engineering of Academy of Military Science PLA China, Beijing 100850, China; moc.liamxof@xdgl-cyc

Zhilong Chen

Ever-growing globalization and industrialization put forward impending requirements for green and sustainable logistics (G&SL). Over the past decades, G&SL initiatives triggered worldwide deliberations, aiming at easing negative transport externalities and improving supply chain performance. This review-based paper attempts to offer a joint quantitative and qualitative understanding for the overall evolutionary trend, knowledge structure, and literature gaps of the G&SL research field. Employing the science mapping approach, a total of 306 major paper published from 1999 to 2019 were retrieved, elaborated on, and synthesized. Visualized statistics regarding publication years, journal allocation/co-citation, inter-country/institution collaboration, influential articles, co-occurred keywords, and time view clusters of research themes were analyzed bibliographically. On this basis, a total of 50 sub-branches of G&SL knowledge were classified and thematically discussed based on five alignments, namely (i) social-environmental-economic research, (ii) planning, policy and management, (iii) application and practice, (iv) technology, and (v) operations research. Finally, the current knowledge obstacles and the future research opportunities were suggested. The findings contribute to portray a systematic intellectual prospect for the state quo, hotspots, and academic frontiers of G&SL research. Moreover, it provides researchers and practitioners with heuristic thoughts to govern transportation ecology and logistics service quality.

1. Introduction

Sustainable development has inspired many green and sustainable logistics (G&SL) activities to reduce the negative effects of freight transportation [ 1 ] and improve positive environmental and social feedbacks. From long-haul heavy-duty logistics to intra-city distribution, road-based freight transportation systems generate tremendous negative externalities in daily operations [ 2 ], including pollutant emissions, congestion, traffic accidents, noise, visual interference, infrastructure failure and resource waste [ 3 ]. Moreover, these negative externalities, together with the disadvantages of logistics system itself (e.g., limited intelligentization, personnel dependence and vulnerability [ 4 ]), further lead to the downgrade of supply chain performance at both enterprise level and regional level. With the rapid growth of logistics demand, the damage grows exponentially, which will eventually bring irreversible impacts to the economy and the whole ecosystem [ 5 ].

The operation management of physical distribution is one of the most significant and challenging sub-issues of the macro supply chain management (SCM) [ 6 ], because it involves real-time scheduling and coordination of hundreds of thousands of packages and containerized goods under a dynamic logistics scenario [ 7 ]. G&SL is defined as the planning, control, management, and implementation of logistics system through the advanced logistics technologies and environmental management, aiming to reduce pollutant emissions and improve logistics efficiency [ 8 ]. G&SL is not only concerned with providing customers with green products or services [ 9 ], but also with the green and sustainability of the entire lifecycle of the logistics process [ 10 ]. Various green logistics modes, activities, and behaviors were proposed and gradually realized from government rules to technological innovations. For example, the construction of green logistics network [ 11 , 12 , 13 ], reverse logistics [ 14 ], emission control [ 15 ], electric freight vehicle [ 16 ], modal shift and multimodal transportation [ 17 ], energy efficiency [ 18 ], collaboration [ 19 , 20 ], outsourcing [ 21 , 22 ], etc. A wide range of topics related to G&SL yielded substantial academic results and considerable practical performance. However, G&SL is still in its infancy and is far from meeting the challenges posed by the complexity of internal cooperation and uncertainties of external markets [ 1 ].

Previous studies reviewed G&SL from different perspectives. By reviewing 115 papers, Zhang et al. [ 10 ] analyzed the combinatorial optimization problems and swarm intelligence technique applied in improving G&SL performance. Qaiser et al. [ 23 ] conducted some brief statistics on the bibliometric information of 40 papers on G&SL. Bask and Rajahonka [ 8 ] mainly reviewed the role of environmental sustainability in multimodal freight transport decision-making. Based on 56 papers, Mangiaracina et al. [ 24 ] summarized the impact of business-to-customer transportation process on the environment. Arvidsson et al. [ 25 ] reviewed the sustainable measures for improving urban distribution efficiency. Pourhejazy and Kwon [ 26 ] conducted a survey on 380 articles published from 2005–2016 and revealed the application status of operations research technique in the supply chain optimization. The literature of green SCM was classified and reviewed by Srivastava [ 4 ] from a reverse logistics angle. This work was further enriched by Fahimnia et al. [ 27 ], who investigated the bibliographical information and trend of a majority of green SCM research through article co-citation network and keywords co-occurrence network.

However, based on the time of publication and the number of papers contained, the existing studies are outdated and incomplete, unable to provide a comprehensive analysis of the booming G&SL research in the past two years. Also, it is more difficult to integrate the multitudinous research directions to build a complete knowledge structure for G&SL. Therefore, it is of great theoretical and practical significance to objectively and quantitatively investigate the overall progress of G&SL.

This study aims to conduct a comprehensive review of the global G&SL literature, so as to explore the state-of-the-art, hotspots and research trend, as well as to build the G&SL knowledge classification system. Specifically, first, tracking and analyzing the evolution of the G&SL research field from (i) publication year and journals; (ii) countries, regions, and organizations; (iii) influential documents; (iv) keywords clustering and research themes. Second, establishing the knowledge taxonomy based on the scientometric results. Third, identifying the research gaps and the future research opportunities.

The novelty of this study lies in two aspects. One is to integrate the science mapping approach into the systematic literature review process to visualize the relationships among the G&SL literature. Science mapping approach is composed of data mining and bibliographic analysis, which can minimize subjective arbitrariness and grasp useful information to facilitate in-depth thematic analysis. Another is that this study further extends the bibliography to illuminate the emerging knowledge branches, gaps, and agendas in G&SL research, which will contribute to the improvement of G&SL practice and research innovation. The findings are expected to provide researchers and practitioners with a panoramic description and in-depth understanding of G&SL research. Additionally, the proposed knowledge structure can also be used as a handbook-like tool to further collect, analyze, and expand knowledge in the G&SL field and to provide references for other innovative logistics initiatives.

The rest is organized as follows. In Section 2 , the outline of research method is introduced. Section 3 presents the results of the data collection and the results of five parts of scientometric analysis. Section 4 proposes the taxonomy of G&SL research based on the keywords clustering and discusses the knowledge branches in detail. The current research gaps and agenda are also identified. Section 5 summarizes the major findings and limitations.

2. Research Method

2.1. overview of review protocol.

This review-based study conducted a systematic investigation on the academic development of global G&SL research with the aids of science mapping. Science mapping is a quantitative analysis approach that uses mathematical statistics and visualization techniques to study bibliographic networks (e.g., academics, institutions, themes, keywords, and journals) in a specific field [ 28 ]. This approach has been widely applied in many academic fields, such as sustainable transportation [ 29 ], environment science [ 30 ], city logistics [ 31 ] and waste management [ 32 ] and can directly synthesize salient findings from the existing knowledge system.

Figure 1 illustrates the detailed research process, consisting of three steps.

The flowchart of reviewing G&SL literature.

In step 1, the statistics was obtained after a comprehensive retrieval from two electronic databases, Web of Science (WoS) core and Scopus. Two rounds of selection were then performed to refine, classify, and encode the documents. The year publication trend, journal allocation and the most cited articles were described.

Four scientometric tests were carried out in step 2, namely (i) Journal co-citation analysis : to identify the most cited journals and the research domains they belong to. This analysis helps to reveal the distribution of published journals and cited journals of the reviewed documents, so as to identify popular journals in G&SL research domain. (ii) Countries/organizations collaboration analysis : to visualize the collaborative research network of G&SL among countries and organizations, so that the readers can quickly understand the partnerships between major research communities and institutions around the world. (iii) Document co-citation analysis : to highlight the influential G&SL articles and the corresponding reference relationships. By analysis of the papers with high citation, the emerging trend of scholars’ research interest to G&SL is easier to grasp. (iv) Keywords co-occurrence analysis : to map out the co-occurred time zone of the hotspots G&SL keywords and cluster them into several research themes. Network analysis of co-occurred keywords is used to clarify the knowledge structure of G&SL as well as to present the research hotspots and potential research opportunities in the future.

In step 3, the hierarchical knowledge structure of G&SL was proposed for thematic discussion.

The text mining software VOSviewer was adopted for science mapping, combining with another software CiteSpace to portray the time view of the clustered keywords based on the same data. VOSviewer, developed by van Eck and Waltman [ 33 ], is a comprehensive bibliometric analysis tool based on Visualization of Similarities (VOS) technology, which has unique advantages in clustering fragmented knowledge from different domains according to their similarity and relatedness. In the visualized networks, a node signifies a particular bibliographic item, such as organization, country, keyword or reference, etc. The node size represents the counting of the evaluated item namely citation or occurrence. Link denotes the co-citation, co-occurrence or collaboration relationship. The metric, total link strength (TLS), is outputted automatically by the software to reflect the correlation degree between any two nodes in the generated networks. A higher value of TLS, the higher importance and centrality of the item has [ 31 ]. Nodes with a high similarity were clustered together and distinguished by colors with other clusters, while the nodes with low similarity should be separated as far as possible. The similarity matrix can be calculated by Formula (1), where c ij is the co-occurred or co-cited times of item i and item j , W i and W j denote the node sizes of item i and item j respectively [ 33 ]. The stopping criterion of VOSviewer mapping is the minimal sum of weighted Euclidean distances of all items in each cluster [ 34 ], which can be expressed by Formula (2), where x i and x j are the positions of the nodes.

For a detailed operation manual of bibliographical experiments using science mapping approach, readers are advised to refer Jin et al. [ 28 ] and Hu et al. [ 31 ].

2.2. Literature Retrieval and Selection

The advanced retrieval function in Scopus and WoS core collection database was used to retrieve the G&SL related papers published during 1999 to August 2019 (see Table 1 ). To ensure the quality of the literature, the document types were restricted to research articles, while other types such as the conference proceeding, book chapter, letter or editorial material were excluded. The preliminary search yielded 1160 records. These records were imported into EndNote software for the first-round inspection to filter out duplicates and unqualified records in forms (e.g., article length and integrity). Additionally, those completely and partially irrelevant studies were removed. For example, an article entitled “Using logistics regression to analyze the sustainable procurement performance of large supply chain enterprises” was not the desired result. A total of 397 records were left after the first-round inspection. Then, the second-round selection was carried out by carefully reading the abstract of each document. The inclusion and exclusion criteria for this round focused on whether the document was consistent with the research topic, i.e., with green logistics initiatives, practices. and other G&SL innovations, rather than broader research, such as production, manufacturing or urban transportation. Unless it has a strong relation with G&SL. In particular, the following topics were excluded: (i) green design on the specialized logistics technology e.g., biomass and biofuel; (ii) business competition and (iii) offshoring and lean production. Finally, 91 records were removed, leaving 306 full-length articles in our review portfolio.

Results of literature retrieval and selection.

3. Scientometric Experiments and Analysis

3.1. chronological publication trend.

Figure 2 displays the number of papers published annually from 1999–2019 in the portfolio. Obviously, research on G&SL was virtually stagnant until 2009, and since 2010, it has increased significantly year by year. By 2018, a staggering 62 articles were searchable. The vigorous development of academic research indicates the expansion of the scope and branch of G&SL. Furthermore, from the publication number and the recent discussed topics of G&SL, it is evident that the public awareness, market acceptance, social demand and real-world practice of sustainable logistics measures are undergoing remarkable ascent.

Year profile of indexed documents.

3.2. Journal Allocation and Co-Citation Analysis

All 306 documents were found in 81 different journals. As shown in Figure 3 , the top 15 journals contributed 155 papers, accounting for 51% of the total. The impact factors of journals were also attached based on the Journal Citation Reports (2018). Sustainability ranks first (35, 11.4%), followed by Journal of Cleaner Production (24, 7.8%), Transportation Research Part D: Transport and Environment (17, 5.6%) and International Journal of Production Economics (13, 4.2%). Among the top 15 journals, eight are from UK, four from The Netherlands, two from Switzerland, and one from Germany. The papers are mainly distributed in the three academic fields of environment, traffic engineering and operations management, but they obviously account for a larger proportion in the environmental science and sustainable field, which is in line with the connotation of G&SL.

Rank of journals in G&SL publication number.

As shown in Figure 4 , among the 12,408 references (corresponding to 2349 different journals), a network of 46 items and 1025 links was formed by identifying the journals that had been cited more than 50 times. In general, the journals that influenced G&SL research are concentrated in three interrelated clusters. First is the operations research (OR), such as European Journal of Operational Research (TLS = 13,076, citation = 494), International Journal of Production Research (TLS = 252, citation = 8260), Expert Systems with Applications (TLS = 4748, citation = 153), Omega (TLS = 5139, citation = 150) and Computers & Operations Research (TLS = 4234, citation = 137), which can offer quantitative methods for the decision-making and optimization issues related to G&SL. The second cluster is transportation research (TR), such as Transportation Research Part A (TLS = 2057, citation = 103), Part D (TLS = 3883, citation = 176), Part E (TLS = 7576, citation = 260), and Journal of Transport Geography (TLS = 2089, citation = 91), which accumulates enormous knowledge towards transportation planning, technology and operations that can enlighten G&SL research from real-life transport demand and practice. The third cluster, including Supply Chain Management (TLS = 6546, citation = 232), International Journal of Physical Distribution & Logistics Management (TLS = 6357, citation = 235) and Journal of Business Logistics (TLS = 2837, citation = 96), etc., reveals that a large amount of G&SL research was conducted based on the research foundation of logistics and supply chain management (SCM). Among all the publications, Journal of Cleaner Production (TLS = 13,799, citation = 555) and International Journal of Production Economics (TLS = 13,903, citation = 495) are the two most co-cited journals. They often act as hubs, integrating the results of OR, TR and SCM with social, environment or economic implications to provide cross-domain knowledge crucial to the diverse development of G&SL.

Mapping of the journals co-cited.

3.3. Countries/Organizations Collaboration Analysis

Table 2 lists the countries or regions that are actively studying G&SL, showing six measurements, including number of publications (NP), TLS, average citation year, total citations, average citation per country/region, and average normalized citation. The average normalized citation was calculated by dividing the total number of citations by the average number of citations published per year [ 34 ]. Figure 5 displays the collaboration network among countries and regions. The minimum number of documents and citations for a country was set at 5 and 30 respectively. Finally, a map with 25 items and 58 links was generated.

Mapping of countries/regions contribute to G&SL research.

Summaries of countries/regions active in G&LS research.

According to Table 2 , G&SL research is widely distributed, especially in Europe, Asia, and North America, which is a field of worldwide concern. Mainland China has the most publications, but the United States has the highest total citation. Other countries/regions such as Italy, Singapore, Hong Kong, and Taiwan present a lower number of publications; however, they keep significant figures of average normalized citation which can strongly express their high influence. Besides, most of the documents contributed by these countries/regions were published in the last three years, which means they are playing an increasingly active role in promoting G&SL.

Two evidence can be observed from Figure 5 . First, based on a partnership, the global G&SL research is divided into four communities. Therein, two communities are leaded by European counties, such as UK, Spain, The Netherlands, and Italy, while the other two communities are “Mainland China-Hong Kong-Singapore” and “United States-India-Australia-Portugal-Taiwan”, dominated by China and USA, respectively.

Second, the international collaboration is not significant. Taking mainland China for instance, about 70 percent of 49 publications are completed entirely by domestic institutions. The Swedish publications do not have any co-authors from other countries or regions. This phenomenon may be due to the large differences in the background and model of G&SL development in different countries [ 35 ]. Moreover, the knowledge gap caused by the wide extension of G&SL and the scattered knowledge structure make the research still focus on the respective fields of researchers, such as sustainable development [ 36 ], environment governance [ 37 ] and transportation planning [ 38 ]. Therefore, at present, the cooperation between academic institutions of different backgrounds has not been widely carried out.

Among the 402 organizations that contributed to G&SL research, those with more than five documents and over 30 citations were built into a network of 22 items and 22 links, as shown in Figure 6 . None of the organizations published more than 10 papers (3% of 306) and the studies were relatively independent. Therefore, it can be argued that no organization has yet been able to lead G&SL research so far. However, some of the institutions located in Asia Pacific and Europe have a higher reputation in G&SL due to higher citations, including the Hong Kong Polytechnic university (Hong Kong, 388 citations), Wageningen University (The Netherlands, 370 citations), Aristotle University of Thessaloniki (Greece, 324 citations), National Chiao Tung University (Taiwan, 330 citations), Iowa State University (USA, 206 citations), University California Berkeley (USA, 160 citations) and Nanyang Technological University (Singapore, 137 citations). In addition, Figure 6 also shows insufficient collaborative research across organizations.

Mapping of global collaboration network among organizations.

3.4. Influential Research Highlight

Through the document co-citation test of the portfolio, the most influential G&SL publications in the past two decades were analyzed and the co-citation network was constructed. In VOSviewer, the minimum number of citations was set to 30 to build a co-cited visual network map of 83 items and 350, as shown in Figure 7 . The nodes in the map denote the documents that were identified by the first author name and the publication year. The colors of the nodes and the links represent the time of publication and the time of two documents that are co-cited, respectively. The co-occurrence of the literature shows an obvious type of “local concentration and overall dispersion”, indicating that some G&SL studies were widely recognized and produced some common ideas and results. Most papers with high citation appeared around 2010, which was a landmark year for G&SL research. The co-citation time series indicate that G&SL knowledge spreads faster and faster.

Mapping of the influential documents and their co-citation relationship.

The top 15 most cited papers are presented in Table 3 , showing their publication year, title, TLS, citation counts and topics. The most cited study was by Dekker et al. [ 39 ], one of the first methodological studies to link the operations research knowledge (such as design, planning, and control) to the field of green logistics. The second is Sheu et al. [ 40 ], whose main contribution is to propose a modeling technique for sustainable logistics operations and management decisions to maximize supply chain profits. These were followed by papers by Lai and Wong [ 41 ] and Ubeda et al. [ 42 ], which focused on using the scenario-based approaches, such as the questionnaire and case study, to evaluate the environmental performance of green logistics practices. The main topics of other highlighted documents involve: (i) management insights from industrial practices [ 43 , 44 ]; (ii) multi-criteria evaluation system for green logistics (e.g., policy [ 45 ], environment [ 46 ], and transportation planning [ 47 ]); (iii) network facilities design and optimization [ 48 , 49 ]; (iv) reverse logistics [ 50 , 51 ]; and (v) enterprise responsibility and third-party logistics [ 52 ].

List of publications with the highest impact in G&SL.

3.5. Keywords Co-Occurrence Analysis

The keywords co-occurrence analysis was conducted to describe the internal composition and structure of G&SL and to reveal the frontiers [ 31 ]. The options “All Keywords” and “Full Counting” in VOSviewer analysis were checked to obtain a holistic intellectual landscape of G&SL research. Before the scientometric test, the keywords, such as “third-party logistics providers” versus “3PL”, “transport” versus “transportation”, which are necessary due to differences in expression, were manually simplified on the original data file. The minimum occurrences of each keyword was set to 4, forming a network of 112 nodes representing keywords (1455 keywords in all documents) and 2067 links, as shown in Figure 8 .

Mapping of co-occurred keywords.

Figure 8 displays the mainstream of research keywords in G&SL and their co-occurrence relationships. Divide these keywords into four clusters and distinguish them with different colors. Therein, Cluster #1 contains 18 items focusing on the practice and management of logistics sustainability (e.g., collaboration, case study and intermodal transportation), while Cluster #2 covers 25 items, concentrating on the environmental issues of freight transport, such as carbon emission, energy consumption and lifecycle assessment. Cluster #3 (34 items) and Cluster #4 (34 items) emphasize on the “model, planning and optimization” as well as the “supply chain performance, development strategy and competitiveness”, respectively.

Table 4 shows the detailed information of the significant keywords. The top 10 most frequently studied and highly connected terms are sustainability (Feq. = 80, TSL = 547), green supply chain (Feq. = 68, TSL = 629), management (Feq. = 58, TSL = 411), model (Feq. = 55, TSL = 394), green logistics (Feq. = 48, TSL = 325), performance (Feq. = 47, TSL = 367), logistics (Feq. = 46, TSL = 299), framework (Feq. = 43, TSL = 356), impact (Feq. = 41, TSL = 312) and reverse logistics (Feq. = 39, TSL = 323). These keywords play a critical role in forming G&SL research topics and connecting major branches of knowledge. According to the metric of average citations, the following keywords, including transportation, environmental sustainability, production, reverse logistics, and efficiency, aroused a lot of attention.

Summaries of significant keywords and theme clusters of G&SL research.

Keyword co-occurrence network is a static expression of a particular area that does not take into account changes over time in the manner that the terms are used [ 54 ]. Figure 9 shows a time zone view of keywords that occur more than eight from 1999 to 2019. Each term is arranged in chronological order to present the trend and interaction of keywords. Studies on management, model and green supply chains had been published extensively before 2005 and had been going for a long time, showing that these early topics are still the hotspots of current research. In contrast, articles related to collaboration, transportation planning, modal shift and stakeholder were published from 2015 to 2017, which are emerging themes discussed frequently in recent years and may become the hotspots of future research. Additionally, a large proportion of the keywords were proposed between 2007 and 2015, indicating that G&SL research was greatly enriched during this period. Table 4 presents the time span of all highlighted keywords.

A time zone view of clustered research themes: 1999-2019.

4. Discussion

4.1. knowledge taxonomy of current research.

Through the aforementioned analysis, the research progress, evolutionary trend, and hot-discussed topics of global G&SL are clarified. However, the generic scientometric results cannot accurately reflect the explicit division of the multifarious knowledge of a domain [ 31 ]. Based on the clustering analysis of high-frequency keywords, a comprehensive taxonomy of G&SL knowledge from 1999 to 2019 was further proposed, and each separated branch was thematically discussed in-depth subsequently. Topics with similar attributes were integrated into different categories of themes and manually renamed to make the taxonomy more compact and easy to understand. Figure 10 demonstrates the mind mapping of G&SL research themes, where a total of 5 alignments and 50 sub-branches are assembled. The number of representative articles of each theme was also attached.

The knowledge taxonomy of G&SL themes.

4.1.1. Evaluation on the Social, Environmental and Economic Impacts of G&SL Initiatives

Nearly a quarter of the literature (71 out of 306 papers) focused on evaluating and quantifying how the potential green logistics initiatives improve the “triple bottom line” (i.e., social, environmental and economic performance, SEE) of existing freight activities. The subjects of these studies were basically originated from four aspects: carbon emission, energy consumption, social sustainability, and external cost-and-benefit. Mattila and Antikainen [ 15 ] provided a backcasting method for the long-term prediction of greenhouse gas emissions and fossil fuel consumption in long-distance freight transport, considering the sustainable goals and policies developed by the EU governments. Similar research was conducted for assessing the U.S. scenario [ 46 ]. A questionnaire survey conducted by Makan and Heyns [ 55 ] found that the pressures from consumer, brand protection, top management, and cost-saving and revenue are the major drivers for freight organizations to implement the sustainable initiatives. Khan et al. [ 56 ] modeled the impact of G&SL performance on the countries’ economic development and macro-level social and environmental indicators. Papoutsis et al. [ 57 ] and Solomon et al. [ 58 ] both maintained that logistics sustainability is closely related to operational efficiency and social acceptance from an economic and environmental perspective. Through the expert scoring, Morana and Gonzalez-Feliu [ 59 ] identified the most prominent factors affecting the sustainability of urban logistics are monetary saving, services quality, and customers’ satisfaction rate (economic), pollution emissions and congestions (environmental), and the number of employment created/destroyed (social). Social and environmental activities play a more important role in promoting sustainable logistics than financial-economic activities [ 60 ]. Rashidi and Cullinane [ 61 ] found that the national logistics industry with high SEE index has the following features: (i) well-planned logistics network infrastructure; (ii) high quality of service operators; (iii) shipments tracing technology; and (iv) efficient timetable scheduling.

Another part of emphasis was given to SEE performance of G&SL based on logistics operations and business. Guo and Ma [ 62 ] evaluated the energy consumption and emission level under different logistics business modes, concluding that the third-party logistics provider and the joint distribution modes have obvious environmental advantages in developing green urban distribution. Wang et al. [ 63 ] found that green logistics performance would impose positive effects to the exporting countries in the international trade. Herold and Lee [ 64 ] investigated the carbon reports disclosed by some giant international logistics enterprises, e.g., UPS, FedX and DHL, and compared their sustainability-related strategies, namely legitimacy-seeking arguments versus energy and emission reduction. In addition, a variety of qualitative analysis measures, such as fuzzy multi-criteria evaluation modeling [ 65 ], data envelopment analysis [ 66 ], and analytic hierarchy processes [ 67 ] were also widely applied to illuminate the logic between SEE performance and G&SL.

Except for the three-dimensional evaluation system, some scholars also analyzed the critical success factors and barriers for G&SL initiative implementation from the SEE perspectives. For instance, Arslan and Sar [ 68 ] found that the managers’ intention towards green logistics initiatives is generally determined by the environmental attitude, perceived behavior control and subjective norm. Besides, government subsidizes [ 69 ] and internalization of externalities [ 70 , 71 ] were considered to be the effective models to reduce negative external cost in the logistics industry, thus promoting the greening process of the logistics market.

4.1.2. Planning, Policy and Management Research of G&SL

This knowledge branch focuses on two basic G&SL topics, (i) the planning, development, and policymaking from industrial level, and (ii) the collaboration strategy and management from project level. For the former, Lindholm and Blinge [ 2 ] indicated that the public support, stakeholder partnership, and excellent management skills are the most significant factors to achieve sustainable development of the logistics industry. The coordination among metropolitan economy, logistics infrastructure investment, and industrial chain upgrading is the essential foundation of G&SL [ 36 ]. Integrating freight activities into the general planning procedure or transport planning is also considered important for the implementation of G&SL. Shankar et al. [ 72 ] quantified the dynamic uncertainties and intrinsic sustainability risks of freight transport and stated that most of the risks were socially induced rather than financially driven. The risks of multimodal green logistics were analyzed by Kengpol and Tuammee [ 73 ]. A system dynamics simulation conducted by Sudarto et al. [ 74 ] revealed that the economic performance of G&SL is directly affected by freight policy, while environmental performance is indirectly affected. Klumpp [ 75 ] proposed two strategies to develop green logistics, namely encouraging public investment and imposing heavy taxes on carbon raw materials.

For the latter, the collaboration and game among logistics service providers (LSP), government, shippers, and enterprises are paid more attention. Commonly, a positive cooperation strategy of stakeholders will significantly improve the operational performance of G&SL [ 76 ] and even the entire supply chain [ 19 ]. Therein, the benefits brought by the collaboration between suppliers and customers [ 77 ] and LSPs-shippers [ 78 ] are particularly salient. The government plays a dominant role in the knowledge dissemination [ 79 ] and economic incentive of greenization [ 20 ], leading to the innovation of logistics technology. Moreover, the shippers’ willingness to pay for G&SL products [ 80 ], the exploitation of green logistics knowledge [ 81 ], as well as the gaps between green logistics demand and supply [ 82 ] also aroused research attention.

Furthermore, several novel business and operational modes of logistics aiming at improving the sustainability in transportation process were proposed, e.g., freight consolidation [ 83 ], smart logistics [ 22 ], and low emissions zones [ 84 ]. The most hotly debated topics are outsourcing and crowd shipping (CS). CS, proposed for the last-mile delivery problem, is a concept that means the parcels and passengers are co-transported along a passenger trip [ 85 ]. According to Ameknassi et al. [ 86 ], freight transportation, warehousing, and reverse logistics are the three major outsourced logistics activities. The outsourcing strategy has proven to be advantageous in reducing energy use, global warming, and supply chain risk, compared with common logistics operations [ 87 ].

4.1.3. Real-World Application Areas and Practices

Over the past decade, research on the G&SL practices were carried out over a broad range, including SCM, reverse logistics (RL), e-commerce, urban distribution, multimodal transport, and other dedicated logistics such as food [ 88 ] and manufacturing [ 89 ]. Much valuable experience and instructions can be obtained from real-world applications. For example, the unsustainability of the supply chain is largely due to the poor logistics practices in the downstream [ 90 ], which specifically refers to transport operation delay [ 91 ], poor communication [ 91 ] and the lack of effective management of carbon footprint [ 92 ]. A sustainable SCM is an effective measure to improve the competitiveness, financial and environmental performance of logistics enterprises. However, this is not absolute, Hazen et al. [ 93 ] believe that some green SCM practices might not necessarily lead to competitive advantage, but make users feel that they are getting low-quality products.

Reverse logistics is convincingly one of the most efficient solutions to reduce environmental pollution and waste of resources by capturing and recovering the values of the used products [ 94 ]. Legislation, social image, corporate citizenship, and market competence force enterprises to integrate RL into their supply chains [ 95 ]. In real-world application, improving RL sustainability and greening process is the primary goal to optimize the overall supply chain performance. Our review found that most green-related RL studies focused on the network design [ 96 ] and system planning [ 14 ]. Other topics are waste recycling management [ 97 ], benefits assessment [ 98 ], reverse operations outsourcing [ 99 ] and social responsibility [ 50 ].

The unsustainability of urban logistics makes it the most urgent goal of greening. Huge logistics demand, such as rapid business-to-business and business-to-customer logistics activities, make freight transportation in big cities face the dilemma of air pollution, poor accessibility, and livability [ 31 ]. The practice of integrating green logistics planning into smart cities construction has been carried out for a long time, especially in Europe, mainly including last-mile delivery [ 100 ], traffic management [ 101 ] and lean logistics [ 102 ].

Compared with G&SL in urban domain, the sustainability issues regarding inter-city or regional logistics are more emphasized on the intermodal application. The shift of road-based modal to other transportation system, such as rail and water has the potentials of ensuring environmental sustainability, flexibility, and cost reduction [ 17 ]. However, despite the encouragement by the government, the practice of intermodal transport is still in a preliminary stage due to the difficulties of infrastructure investment [ 103 ].

4.1.4. Emerging Technologies Proposed for G&SL Development

Developing advanced facilities and technologies is a sustainable and forward-looking solution to meet the challenge of freight transport. Many emerging logistics systems were proposed in recent years. Such as urban consolidation center [ 104 ], electric road system [ 105 ], intelligent transportation system [ 106 ] and packaging benchmarking system [ 107 ], etc. Meanwhile, some soft applied techniques, such as big data [ 108 ], internet of things [ 109 ] and cloud computing platform [ 110 ], have also been applied to logistics operations to support the sustainable development of the emerging systems.

Electric vehicles (EVs) technology, which has been widely applied in passenger transport, is also waving a revolution in the field of G&SL. Current research on freight EVs mostly focuses on energy efficiency [ 111 ], fleet optimization [ 16 ] and environmental benefits [ 112 ]. Simulation results from various cities show that EVs achieve extremely high benefits in carbon emission reduction, with over 80% relief rate tested by Giordano et al. [ 112 ].

For reducing the negative externalities such as traffic congestion and disturbance, another interesting concept, i.e., transferring the ground logistics process to underground space, namely the Underground Logistics System (ULS), has aroused increasing attention. ULS refers to using a group of hierarchical underground nodes, pipelines, and tunnels to distribute cargo flows in and between cities with 24-h automated operations [ 113 ]. ULS can be designed as a network form connecting urban logistics parks and last-mile delivery, or a dedicated underground container line established between seaports and urban gateways, leading to huge environmental and social benefits (e.g., energy-saving, accidents and congestion mitigation and improving urban logistics capacity, etc.) [ 114 ]. So far, the technological feasibility of several ULS projects was acknowledged, yet the large-scale implementation has not started due to the relatively high construction cost and low public awareness [ 5 ]. For this reason, the collaborative strategy of retrofitting existing urban rail transit systems, such as trams, light rail or subways, to achieve mixed passenger-and-freight transport has received higher recognition and was successfully stepped into engineering practice in some European cities [ 115 ]. Compared with ULS, the collaborative modes are easier to implement, since the dual use of transportation infrastructures would moderate the system cost to an acceptable level [ 49 , 116 ].

4.1.5. Operations Research and Optimization Methods for G&SL Decision-Making

The operations research (OR) of G&SL issues that are originated from real-world applications is always being a well-concerned topic because it is directly related to the quality of some critical decision-making in logistics operation. The OR method applied for G&SL is defined as a better of science to identify the trade-offs between environmental aspects and costs, so that the corresponding decisions such as location, transportation, warehousing, and inventory can be optimized and the limited resources can be reasonably assigned [ 39 ]. Dekker et al. [ 39 ] classified the application of OR in green logistics as follows: logistics services network design [ 48 ], facility location [ 117 ], vehicle routing problem [ 118 ], inventory management [ 40 ] lifecycle production optimization [ 119 ], supply chain planning, control, and procurement [ 120 , 121 ] and model choice [ 122 ]. A variety of OR techniques, such as heuristic algorithms [ 121 ], stochastic programming [ 53 ], and robust optimization [ 123 ], were developed for the above issues. In addition to the objectives of general logistics planning e.g., cost and efficiency, the G&SL version focus more on the minimization of environmental influence, e.g., carbon emission and energy consumption. Currently, OR is increasingly applied to optimize the G&SLs’ decision-making in a complex scenario set, such as demand uncertainty [ 48 ] and facilities failure [ 124 ].

4.2. Research Gaps and Agenda

Through the above scientometric analysis and thematic discussion, the comprehensive research trend, mainstream academic topics, and knowledge taxonomy of G&SL domain were revealed. Although researchers and practitioners achieved substantial results in promoting G&SL theory and practice, there are still some shortcomings that need to be elaborated in future studies.

4.2.1. Limitations of Global Collaboration and General Evaluation Framework

In terms of research model, international cooperation is still lacking. The broad applicability of most G&SL knowledge based on local cases deserves further discussion, such as planning methods and evaluation systems. European countries made great efforts in rebuilding the integration of green logistics. However, the lack of international cooperation and universal solutions hinders the dissemination and deepening of knowledge, and the current achievements are far from enough to promote the globalization of G&SL, which is reflected in the imbalance of global G&SL practice.

To fill this gap, although it is recognized that logistics policy has a strong regional character, cross-institutional and cross-national collaborative research on market operation, industrial metrics, technology innovation and macro development strategies should be strengthened under the trend of supply chain globalization. For example, more attention can be paid to the horizontal comparison of green logistics mode, scheme and performance under different case backgrounds. Additionally, more empirical studies are needed to be carried out in some developing countries in Asia and elsewhere in the world, considering they are the fast growing economies with higher population and logistics demand.

4.2.2. Complement Research from a Global/Holistic Perspective

Although the knowledge branch of research is flourishing, it is acknowledged that there is still a need to supplement the overall or holistic research to improve the knowledge system of G&SL. Research on sustainability and green has always been complex and multi-variable, interactive, with far-reaching implications. Besides, sustainability and green are public and social issues. Current theoretical applications are limited to the analysis of local or one-way relationships, such as LSP/retailer/carrier responses to green policies, planning and performance evaluation of green and sustainable initiatives.

The operation and decision-making of G&SL involve many stakeholders, such as local authorities, manufactures, LSP, carriers, customers, and even the sharers of transportation resources. The impact of G&SL should also be long-term and dynamic. Thus, the whole picture includes multiple perspectives, such as the dynamic evaluation of the whole life-cycle of green logistics practice, the decision interaction among multiple stakeholders, and the follow-up research and report on a new green technology or practice.

4.2.3. Lack of Effective Platform to Accelerate the Research of Innovation Technology

Without green innovation technologies, the effect of implementing G&SL from a management perspective alone is minimal. However, it takes a lot of time for some innovative technologies that can fundamentally improve the negative effects of logistics to move from laboratory to application. Applications such as the EV took decades to implement [ 125 ]. Although the technology is constantly updated and improved, more management lag. Another competent concept, the ULS, ASCE has published a feasible technical system as early as 1998 [ 126 ], but only in a few countries has it been publicly piloted in recent years.

The introduction of a new thing does require a long period of demonstration, such as the reliability of the technology, the acceptability of the market and the ambiguity of the real benefits. However, the problem is often the gap and lag in the research of application management in the transition from technical problems to market application and practice management. Therefore, building effective platforms based on multidisciplinary, cross-organizational collaboration to accelerate the research and application of innovative technologies is particularly important for G&SL practices, such as ULS, RL, and CS. Such calls are all the more urgent in their own research.

5. Conclusions

The concept of green and sustainable logistics has received increasing attention and consideration government sectors, scholars, practitioners, and international organizations. A large amount of practical achievement was made at both the industrial and theoretical levels. This study reviewed 306 valuable contributions regarding G&LS over the past two decades through a three-step review program. They were described in year publication, journal allocation and citation counts. Then, the bibliographic networks of countries, organizations, journal and document co-citations, keyword co-occurrence and timezone clusters of research themes were visualized to help understand the overall research status and academic progress worldwide. Grounded in the scientometric analysis, an integrated knowledge taxonomy of the G&SL field was presented, including five major alignments and 50 sub-branches.

Results indicate that the chronological publication of G&SL shows a trend of rapid increase. The quantity of literature published in 2018 is fifteen times more than that of 10 years ago. Sustainability , Journal of Cleaner Production , Transportation Research Part D: Transport Environment and International Journal of Production Economy are the top four journals, which contributed over a quarter of all G&SL papers since 1999. The maps of journal allocation and co-cited journals show that the current research is most relevant to the environmental science and transportation science. In terms of countries, China, the United States, the UK, Sweden, and India are the major territories of G&SL research. The network across co-authored organizations and countries revealed that the collaboration among different research communities is not strong. Hence an active and robust global collaboration atmosphere has not formed yet.

The map of co-occurred keywords showed that the most frequently discussed G&SL themes in each cluster were sustainability and management (cluster #1), freight transportation and carbon emission (cluster #2), model and reverse logistics (cluster #3), and green supply chain and green logistics (cluster #4). The timezone view of keywords showed that articles related to collaboration, transportation planning, modal shift and stakeholder were largely published during the recent years. On this basis, the knowledge taxonomy of G&SL was manually synthesized from five aspects: (i) evaluation on SEE impacts of G&SL initiatives; (ii) planning, policy, and management research; (iii) real-world application areas and practices; (iv) emerging technologies and (v) operations research and optimization methods for G&SL decision-making.

Finally, the potential roadmap for filling current research gaps was recommended, which were divided into three streams: (i) more global research collaboration should be advocated to jointly develop and supplement the comprehensive evaluation framework of G&SL performance; (ii) future research efforts could focus on the interactive and dynamic relationships among sustainable development goals, green policies and the decision-making of multiple stakeholders; (iii) the application-oriented platforms and management research for some most advanced green logistics initiatives would be highly beneficial in promoting G&SL innovation.

However, it should be noted that the data used in this study was confined to those research articles and review articles that were published in the peer-reviewed journals, and they were retrieved only from the two mainstream databases considering the applicability of software. Although the indexed documents could represent most of the convictive viewpoints of G&SL research, some valuable articles that were published in other forms or included in other databases might be overlooked inevitably. To sum up, this review has great room for improvement in terms of material selection. A systematic investigation incorporating valuable conference proceedings, reports, and books in the field of green logistics or green supply chain is expected to portray a more comprehensive knowledge map for future research. Additionally, the in-depth review of the hotspot themes in G&SL domain e.g., OR application and SCM, may also contribute to multidisciplinary integration and interaction.

Acknowledgments

The editors and anonymous reviewers of this paper are acknowledged for their constructive comments and suggestions.

Author Contributions

R.R. and W.H. proposed the research framework, analyzed the data and wrote the article; B.S. and Y.C. contributed to data collection; J.D. and Z.C. contributed to revising article. All authors have read and agreed to the published version of the manuscript.

This work was supported by the National Natural Science Foundation of China (Grants no. 71631007, no. 71601095 and no. 51478463), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant no. SJCX19_0230).

Conflicts of Interest

The authors declared that they have no conflicts of interest to this work.

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Please note you do not have access to teaching notes, green supply chain management: practices and tools for logistics competitiveness and sustainability. the dhl case study.

The TQM Journal

ISSN : 1754-2731

Article publication date: 9 March 2015

Globalization has led worldwide organizations to balance their economic and environmental performances in order to achieve a concrete sustainable development. In an environmental centered world, logistics is called to put into action advanced programs based on technological and organizational improvement, in order to gain or maintain a concrete competitive advantage. The purpose of this paper is to investigate how logistics organizations try to face the recent ecological challenges and the role that the emergent green technologies play in making them finally “green” and competitive.

Design/methodology/approach

Green supply chain management (GSCM) practices have been investigated to better understand their influence on economic performance and corporate competitiveness. After providing a background discussion on Green Logistics and GSCM, the authors have also identified specific research questions that are worthy of investigation, also thorough the DHL case study. The case study analysis has been conducted according to a specific conceptual model (Rao and Holt, 2005), which allows a deeper understanding of literature review results.

The present paper offers some insights on innovation influence on supply chain management (SCM) greenness, a process oriented to a sustainable and environmental-friendly approach to management of supply chain. According to DHL case study evidence, in logistics innovation, often based on emerging green technologies, is strictly related to the development of a much more sustainable and environment-friendly approach to SCM, based on reduction of core activities’ ecological impact, cost saving, quality, reliability, performance and energy efficiency. In this context, the respect of environmental regulations is fundamental to achieve not only a reduction of ecological damage, but also to overall economic profit.

Research limitations/implications

There is a concrete need of further research to better understand the potential link between GSCM, green innovation and logistic organizations competitiveness. In fact, this research area still represents a source of interesting challenges for practitioners, academicians and researchers. Concluding, the research findings cannot be generalized to all logistic organizations, even if DHL is on of the most important and globalized logistic companies. Future researches should empirically test the achieved results also through comparative studies based on a large sample.

Originality/value

The suggestion of literature review and the result of case study analysis represent a first attempt to better understand the real and potential influence of GSCM on corporate image and competitiveness. In fact, the present investigation has pointed out that logistic organization can achieve environmental goals and acquire a better positioning than their competitors also cooperating with stakeholders. Therefore, it is necessary that organizations contribute to make them able to participate in corporate activities and develop a concrete environmental-friendly orientation, based on the respect of market’s requests and environmental regulations in order to get their corporate reputation strong than ever.

• Sustainable innovation
• Green Logistics
• Green supply chain management

Cosimato, S. and Troisi, O. (2015), "Green supply chain management: Practices and tools for logistics competitiveness and sustainability. The DHL case study", The TQM Journal , Vol. 27 No. 2, pp. 256-276. https://doi.org/10.1108/TQM-01-2015-0007

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Practicing green logistics in supply chain management.

Photo: iStock.com/ AlexBrylov

In recent years, the supply chain industry has undergone a major shift toward “green” practices. The push for sustainability comes with many built-in benefits, including lower costs, increased efficiency and stronger customer loyalty.

Green logistics uses a combination of technology and management tools to create a more sustainable supply chain. Relevant practices include shifting to renewable energy sources; transitioning to fleets of electric vehicles; optimizing routes, fuel efficiency and loads; improving maintenance practices; adopting recycled packaging materials, and monitoring aggressive driving.

Making the transition to green logistics isn’t always easy. There are a number of challenges to be overcome, before companies can successfully implement sustainable technologies. While some in the logistics field are already shifting to a green approach, others are slow to adopt new strategies.

Beyond a natural resistance to change, implementing green logistics can be expensive. Electric vehicles cost  up to three times as much  as traditional models. When you factor in the cost of building renewable charging stations, that figure is even higher. And in most parts of the world, biofuels cost more than fossil fuels.

Down the road, expect many of those costs to be offset by efficiency gains. And the technology will become more affordable with time.

Public perception can be a strong incentive to adopting green logistics. According to a McKinsey study, some buyers are willing to pay between 5% and 10% more for sustainable logistics. It’s important, then, to inform customers about your sustainability efforts.

Green logistics can also boost operational efficiency. Route and load optimization, and the elimination of aggressive driving and unnecessary idling, can reduce pollution while cutting costs. In the end, by pursuing sustainability, companies can actually reduce the long-term expense of shipping and transportation.

Following are the most promising trends in sustainable supply chain management.

Adopting renewable energy in the warehouse.  According to the United Nations , 29% of the world’s electricity comes from renewable sources. Warehouses are part of this trend, as they shift to solar, wind and biomass power in order to reduce their carbon footprint and cut costs. Solar power is relatively easy to scale — companies can simply add more solar panels as needed — while wind turbines are easy to place around the exterior of the warehouse as needed.

Renewable energy also mitigates the risk of sudden power outages from natural disasters. And it promotes energy independence, by freeing companies from reliance on the grid.

Optimizing fuel efficiency.  The  U.S. Department of Energy  notes that aggressive driving can reduce fuel efficiency by as much as 40%. Idling, according to the Department of Energy, can use up to half a gallon of fuel per hour.

Fleet telematics  can help companies optimize fuel efficiency, by identifying where waste occurs during transportation. Operators should use the technology to take a close look at fuel consumption, aggressive driving and idling trends over the span of multiple months.

Deploying the IoT and real-time monitoring for resource efficiency.  Internet-of-things devices like smart sensors can monitor energy usage, then issue alerts when, for example, lights are left on in unoccupied buildings, or heating and cooling systems are being overused.

Adopting green certifications and compliance.  ISO 14001  is the recognized international standard for sustainable logistics and transportation. The certification can be key to boosting a business’s public image and enhancing customer loyalty. In addition, by complying with a host of domestic and international regulations on sustainability, companies can avoid heavy fines and other penalties.

Shifting to electric vehicles.  Electric cars, trucks and buses cost more than traditional vehicles, but over time they may save companies money. That’s especially the case when they’re powered by electricity from renewable sources.

The use of electric vehicles can also save companies from fines. In California, for example, the  Clean Trucks Check program  sets emissions standards for trucks and other heavy vehicles. Electric vehicles are generally compliant with the regulations, while many older vehicles are not. Additionally, EVs offer operational cost savings over internal combustion engines.

Embracing circular economy practices.  A circular economy reduces waste by extending the lifespan of every material and product. In the supply chain, this means reusing packaging materials like plastic, metal, and cardboard; choosing biodegradable materials wherever possible, and repurposing products and materials at the end of their lifespan.

Modern technology tools have made supply chain transparency easier than ever before. The blockchain, for example, enables the tracking of goods and materials, while IoT sensors can track products and vehicles, as well as monitor driver behavior. Systems employing artificial intelligence can help operators to accurately forecast demand, avoiding the over- or understocking of materials.

In the future, expect to see much wider adoption of technologies like AI and blockchain. As the supply chain becomes more transparent, companies will place an increased emphasis on sustainable material sourcing and fair trade.

Ultimately, greening the supply chain means transforming every stage of the process, in order to reduce carbon emissions, increase resilience and create more efficient systems.

Graham Perry is a writer at  Business Tech Innovations .

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• Published: 27 July 2024

Optimization research on multi-trip distribution of reverse logistics terminal for automobile scrap parts under the background of sustainable development strategy

• Hongyu Wang 1 ,
• Huicheng Hao 1 &
• Mengdi Wang 1  

Scientific Reports volume  14 , Article number:  17305 ( 2024 ) Cite this article

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• Applied mathematics
• Engineering
• Sustainability

To effectively solve the reverse logistics distribution problem caused by the increasing number of scrapped parts in the automotive market, this study constructs a multi-trip green vehicle routing problem model with time windows by comprehensively considering the coordination between carbon dioxide emissions and cost efficiency. A hybrid adaptive genetic algorithm is proposed to solve this problem, featuring innovative improvements in the nearest neighbor rule based on minimum cost, adaptive strategies, bin packing algorithm based on the transfer-of-state equation, and large-scale neighborhood search. Additionally, to efficiently obtain location data for supplier factory sites in the distribution network, a coordinate extraction method based on image recognition technology is proposed. Finally, the scientific validity of this study is verified based on the actual case data, and the robust optimization ability of the algorithm is verified by numerical calculations of different examples. This research not only enriches the study of green vehicle routing problems but also provides valuable insights for the industry to achieve cost reduction, efficiency enhancement, and sustainable development in reverse logistics.

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Introduction.

Driven by scientific and technological innovations and economic development, the world automobile market has been expanding rapidly in recent years, and the number of automobiles in the market has been rising gradually 1 . In addition, consumers' increasing demands for quality and innovative performance of automobiles have prompted automobile manufacturers to introduce more diversified model categories to meet the ever-changing market demands. This trend has forced the choice of materials, production processes, and quality control in the automobile manufacturing process to become increasingly demanding. Many of the reasons mentioned above ultimately lead to a significant increase in the number of faulty parts produced by users during the warranty period and the number of defective products produced by automotive companies during the manufacturing process, which are collectively referred to as automotive scrap parts in this paper. The automotive industry is a typical resource-intensive industry, and more than 90% of the steel and nonferrous metals on the parts are recyclable, which can bring considerable economic benefits 2 . Therefore, the recycling and remanufacturing of scrap parts materials have positive impacts on the return of funds to the automotive industry system and the implementation of the government's sustainable development strategy, and has become a research hotspot in the field of remanufacturing in various countries 3 , 4 . At the same time, the reverse logistics recycling process of scrap parts has also become an issue of great concern in the field of environmental resource protection and green logistics 5 .

For the world's largest automobile producer like China, the recycling of scrap parts for remanufacturing recovery is particularly urgent 6 . According to China’s National Bureau of Statistics (NBS), as of the beginning of 2023, China’s civilian ownership of automobiles amounted to 319 million, ranking first in the world. In recent years, the scrapping rate of automobiles is about 3.5%, from which it can be approximated that the scale of China's automobile scrapping parts is gradually expanding, as shown in Fig.  1 . The Chinese government has also launched a number of policies around 2020 to regulate the recycling mechanism of scrap parts and strengthen the guidance of the industry's sustainable development practices. At present, China's major automobile companies and their parts suppliers have strengthened their efforts to handle scrap parts to accelerate the return of capital and respond to the call for a sustainable development strategy.

Automobile scrapping trend in China.

To achieve technological spillover, reduce costs, and improve competitiveness, the automobile industry usually adopts the development mode of industrial clustering, which means that many automobile parts suppliers have set up manufacturing factories around the locations of automobile manufacturing enterprises. Therefore, the reverse logistics terminal for scrap parts usually has a corresponding logistics center to undertake the task of receiving scrap parts from vehicle manufacturing plants and automobile service stations around the world, and delivering them to the suppliers, as shown in Fig.  2 . The importance of logistics terminal distribution is becoming more and more prominent. However, as the number and diversity of scrap parts continue to increase, logistics centers are facing greater operational pressure, and many problems are gradually being exposed. Among them, the most common problems include the waste of resources by adopting a point-to-point mode for terminal distribution, the difficulty of coordinating the high inventory level, and the lack of scientific and reasonable planning for vehicle scheduling relying on the experience of drivers.

Reverse recycling logistics network.

In previous studies, experts and scholars on automotive reverse logistics have focused on scrap parts recycling policy 7 , 8 , reverse logistics network design and planning 9 , 10 , and reverse logistics network efficiency evaluation 11 , 12 . For the reverse logistics terminal of automobile remanufacturing, especially in the logistics terminal distribution for parts suppliers in industrial cluster areas, there are fewer researches in the past, and there is a lack of relevant theories and case studies. Therefore, the purpose of this paper is to establish a new model of terminal distribution of automotive scrap logistics that can be adapted to the context of sustainable development. To effectively realize the coordination between environmental cleanliness and enterprise cost-effectiveness, ease the inventory pressure of the automotive scrap logistics center, and promote the efficient operation of the distribution network. At the same time, we analyze the effectiveness of this study in actual operation to create a benchmark case and provide a case reference for the transformation and upgrading of the industry. The main contributions of the study are as follows:

In terms of model construction, this paper intervenes to analyze the terminal distribution mode of automobile scrap parts reverse logistics through practical cases. Under the background of sustainable development strategy, carbon emission, overall logistics costs, and delayed delivery rate are introduced as the optimization objectives, and the multi-trip green vehicle routing problem model with time windows (MTGVRPTW) is constructed.

In terms of algorithm design, this paper combines the nearest neighbor rule based on minimum cost, adaptive strategy, bin packing algorithm based on transfer-of-state equation, large-scale neighborhood search algorithm and genetic algorithm to design a hybrid adaptive genetic algorithm to solve the MTGVRPTW problem. At the same time, the adaptive genetic algorithm, genetic algorithm, and hierarchical particle swarm algorithm are used to conduct comparative experimental analysis to verify the validity and advancement of the algorithm.

In terms of factories location data collection, this paper proposes a coordinate extraction method based on image recognition technology that is feasible within a specific range, replacing the traditional method of converting latitude and longitude coordinates to plane rectangular coordinates, improving the efficiency of data collection.

This paper discusses the effectiveness of the proposed method in actual operation based on practical case study, which provides substantial reference value for the solution of reverse logistics terminal distribution problems in the industry.

The other sections are organized as follows. “ Literature review ” section reviews literature studies on related topics. “ Problem description and formulation ” section describes the problems in the case and constructs a mathematical model. “ Solution methodology ” section describes the design process of the solving algorithm. “ Computational experiments ” section discusses the numerical calculations of practical cases and simulation examples.

Literature review

As the concept of sustainable development continues to spread, the optimization and design of reverse logistics systems have attracted the attention of many scholars. Facing the optimization and design problem of the reverse logistics network of waste batteries in Turkey, Kilic et al. 13 proposed a two-stage multi-objective optimization method, achieving an effective combination of economic and environmental benefits. In terms of logistics network structure design, Sun et al. 14 focused on the e-commerce closed-loop supply chain network under uncertain environment. They used a robust point-to-point optimization method to establish a robust optimization model to reduce the negative impacts of uncertainties in forward and reverse logistics on the logistics network. Faced with the problem of reverse logistics of infectious healthcare waste in the context of the COVID-19 pandemic, Yaspal et al. 15 developed an optimization model considering cost-effectiveness and risk avoidance, using data-driven digital transformation to manage disposable medical waste.

The reverse logistics distribution problem studied in this paper essentially falls into the category of the vehicle routing problem (VRP), first proposed by Dantzig and Ramser 16 , which is an NP-hard problem. After conducting a literature review on related topics, we found that there has been limited research on the vehicle routing problem in reverse logistics. Regarding the recycling and reuse of recyclable waste, Cao et al. 17 studied the vehicle routing problem of a two-echelon collaborative reverse logistics network. Aiming to minimize total costs and considering constraints such as vehicle load, they established a heterogeneous electric vehicle routing model with time windows and designed an intelligent optimization algorithm for solving problems efficiently. For the reverse logistics system of urban sorted waste, Hong et al. 18 studied the joint decision problem of transfer station location and vehicle route planning. Their model considered greenhouse gas emissions and distribution costs, and they proposed a fast hybrid heuristic algorithm based on column generation and adaptive large neighborhood search, which effectively solved the problem. In the reverse logistics problem of kitchen waste, Shi et al. 19 studied the problem of the location of processing center and route planning, incorporating carbon trading policies into the model. Their scenario analysis of transportation capacity and methods concluded that the larger the capacity of electric trucks, the greater the economic and environmental benefits. Regarding the reverse logistics of construction waste, Chen et al. 20 focused on multi-depot vehicle routing problems with transport time windows of collision risk. They proposed cost-effective and environmentally friendly transport plans and developed an intelligent optimization algorithm for problem-solving. In considering dynamic energy consumption for multi-center mixed fleet reverse logistics distribution, Li et al. 21 conducted research on mixed fleet operating costs, charging station insertion strategies, and algorithm design. Their method demonstrated outstanding results in reducing total cost expenditure and improving average customer satisfaction across 15 sets of case experiments. Regarding the reverse logistics distribution of end-of-life electronic products in South Korea, Kim et al. 22 studied vehicle route planning operation modes with the objective of reducing transportation distance. They constructed a sub-vehicle routing problem for each regional center and designed a Tabu search algorithm for effective problem-solving.

A comprehensive review is conducted after combing through the relevant literature in the above research areas. First of all, from the perspective of research scenarios, there are fewer practical case studies on engineering applications, especially for the reverse logistics distribution of scrap parts returned to the factory for remanufacturing in automobile industry cluster areas, which have not been studied. Automobile scrap parts have high potential value, and there is an urgent need for academics to deal with this special scenario to provide the industry with a combination of theory and engineering case reference.

Secondly, regarding research models, previous studies have primarily focused on optimization objectives such as cost, carbon emissions, and customer satisfaction. However, the calculation methods for these objectives still need further study, particularly in addressing the mutual coupling issues among the objectives. In the VRP field, the design of optimization models often requires personalized analysis combined with specific application scenarios and cannot be generalized directly from different research contexts. The green distribution problem (GVRP) is particularly considered in this study, and there are a certain number of research results in the field of GVRP, which can provide a reference for the study of this paper. For example, for the green vehicle routing problem with simultaneous pickups, Olgun et al. 23 focused on reducing fuel consumption costs and meeting demand from customers in both pickup and delivery. They proposed a hyper-heuristic algorithm based on iterative local search and variable neighborhood descent heuristics to solve the problem. Regarding the green heterogeneous vehicle routing problem, Behnamian et al. 24 specifically considered the location and time of vehicle refueling while reducing carbon dioxide emissions, and designed a data mining-based firefly algorithm to solve the problem.

In addition, in terms of distribution mode selection, previous studies have considered multi-center and mixed fleet operation modes, but the integration of multi-trip distribution modes has not been adequately discussed. Assigning a single-trip delivery task to a single vehicle leads to substantial resource consumption, which is not conducive to achieving clean production. Some studies on multi-trip distribution are also worth noting. For example, inspired by urban waste collection practices, Huang et al. 25 introduced a new multi-trip vehicle routing problem with time windows and proposed a branch-and-price-cut algorithm to solve the problem. In addressing distribution issues in the beverage logistics industry, Sethanan et al. 26 considered multi-trip and heterogeneous fleet distribution modes. They proposed an integer linear programming formulation and a hybrid differential evolution algorithm combining genetic operators and a fuzzy logic controller.

Problem description and formulation

This section discusses the RT logistics center as a typical industry case study. Firstly, we analyze the original operation mode and the exposed problems. Then we propose an improved logistics terminal distribution mode. Finally, we construct a mathematical model based on the improved mode to solve the terminal distribution problem.

Describe the case and analyze the problem

The automotive scrap parts logistics center is responsible for receiving scrap parts from automotive service stations around the country and delivering them to the suppliers. RT logistics center mainly has the following functions: collection, sorting, inventory, storage and custody, and distribution. In the original distribution model, the RT logistics center adopts a point-to-point batch pickup model with suppliers, as shown in Fig.  3 . That is, when the suppliers' materials in the logistics center reach the set inventory level, the logistics center will send a pick-up notice to the supplier, and then the supplier will arrange its vehicle to pick up the materials and send them back to the factory. Through field research, we found the following problems in the logistics center under the original model:

The point-to-point bulk pickup model leads to high inventory levels, which results in expensive inventory costs. At the same time, excessive inventory takes up a large amount of storage space, making it difficult to adapt to future growth within the rapidly expanding automotive market.

The limited scale of the supplier's self-pickup model leads to high transportation costs and low logistics efficiency, resulting in high logistics costs and high levels of carbon emissions for the entire distribution network.

Since multiple suppliers are involved, it is difficult to coordinate the vehicle models and pick-up times of each supplier, this can easily lead to confusion in the management of the logistics center, interfering with the normal operation status of the outbound storage link.

Original distribution mode.

Designing improved distribution mode

Under the original distribution model, a direct way to reduce inventory levels was to increase the frequency of supplier pickups. However, the rational decision-makers of the suppliers, are not willing to bear more logistics and transport costs while their interests remain unchanged. Therefore, this paper decides to improve the logistics system from the perspective of changing the point-to-point transport mode to reduce the logistics inventory level and the overall logistics costs of the whole supply chain. It will also help to reduce the carbon emissions in the logistics process and support the implementation of green sustainable development policies.

The Milk-run model is a point-to-group efficient delivery method, which many scholars 27 , 28 have applied in production research. Studies have shown that the adoption of this model can reduce transport and inventory costs by increasing the loading rate of transport vehicles, thus achieving a reduction in the total costs of logistics. In view of its characteristics of “multi-frequency, small batch, and fixed time window”, it can be a better solution to the problems existing in the RT logistics center under the original model. Therefore, this paper decides to establish a kind of circular distribution network based on Milk-run with the logistics center as the leader, as shown in Fig.  4 , and then combines it into the MTGVRPTW for in-depth study.

Improved delivery mode.

Model assumptions and symbol description

Model assumptions.

In the actual transportation and distribution process, the vehicle will be affected by a variety of uncontrollable factors, so this paper makes the following assumptions about the mathematical model: (1) Sufficient capacity to take on the distribution needs of suppliers. (2) The transport process is not affected by weather, traffic control, travel peaks, etc., and always maintains the set average speed at an even pace. (3) After each trip delivery departs from the logistics center, it serves the customers in turn according to the optimization results and returns to the logistics center upon completing the delivery task. (4) Each distribution trip can serve multiple suppliers, but each supplier set cannot be delivered by multiple distribution trips. (5) Distribution vehicles are subject to the double limitation of carrying capacity and loading space, which cannot exceed the constraints limitations, and the loading space is expressed in the form of the number of loading units that can be loaded. (6) The vehicle stays at each site for the same time.

Symbol description

The symbols used in the MTGVRPTW model constructed in this paper and their related descriptions are shown in Tables 1 and 2 .

Decision variables

Mathematical model

Objective functions.

In the context of cleaner production and sustainable development strategies, when building the MTGVRPTW model for automotive scrap parts distribution, it is necessary to consider the impacts of various factors, including: (1) Reducing ecological impacts. (2) Achieving cost reduction and efficiency in logistics. (3) Ensuring the timely delivery of distribution services. On this basis, this study proposes three different optimization objectives.

Minimizing carbon dioxide emissions

The ecological impact of the vehicle distribution process is usually caused by the fact that driving a vehicle consumes fuel and produces carbon dioxide. Therefore, the first optimization objective in the model is set to minimize carbon dioxide emissions during the delivery process. Zhou et al. summarized most of the estimation methods on carbon emissions from automobiles 29 , considering the difficulty of obtaining data, this paper decided to use the fuel consumption rate measure to calculate. The specific formula is shown in Eq. ( 1 ).

Minimizing overall logistics costs

In the process of logistics distribution, a variety of transportation resources need to be deployed, which will generate several costs, including internal preparation, vehicle rental, driver labor, and distance transportation. The goal of the actual operation of the enterprise is to reduce the overall logistics costs and improve the efficiency of resource utilization. Therefore, this paper sets the lowest overall logistics costs as the second optimization objective. The specific formula is shown in Eq. ( 2 ).

Preparation costs for departure

Before the departure of each trip within the enterprise involved in the shelves, moving storage, loading and other logistics arrangements, including a large number of logistics equipment, material and human resources, the unified deployment. The costs of this item is shown in Eq. ( 3 ).

Vehicle rental costs

Distribution vehicles required for distribution are obtained by leasing with third-party companies, which generates vehicle leasing costs. The final decision on the number of vehicles to be rented is related to the result of combining multiple trips. The costs of this item is shown in Eq. ( 4 ).

Driver labor costs

Each vehicle is provided with a driver, who is employed on a temporary basis. If the actual delivery time of the vehicle m is less than the legal working hours of half a working day (4 h), a half-day contract is concluded with the driver of the vehicle; otherwise, a full-day contract is concluded. The costs of this item is shown in Eq. ( 5 ).

where CH m represents the labor costs of equipping the vehicle m with a driver and is related to the travel time of each vehicle. It is shown in Eq. ( 6 ).

where the formula for the travel time of each vehicle is as described in Eq. ( 7 ).

Vehicle transportation costs

Transportation costs will be generated when transporting goods, which include fuel costs, etc. And it is directly proportional to the distance traveled. The costs of this item is shown in Eq. ( 8 ).

Minimizing delayed delivery rates

If the delivery vehicle arrives at the destination earlier or later than the time window required by suppliers, it may disrupt the supplier's normal work situation. Therefore, we expect the delayed delivery rate to be minimized to avoid disrupting the supplier's work schedule due to unfavorable delivery. The specific formula is shown in Eq. ( 9 ).

where constraint Eq. ( 10 ) restricts the allocation of each site to only one trip. Constraint Eq. ( 11 ) limits the number of times a single vehicle can enter and exit the logistics center under multi-trip distribution to the same number of times. Constraint Eq. ( 12 ) restricts the number of times a vehicle can drive in and out of the same stop to remain equal. Constraint Eq. ( 13 ) restricts that no distribution task on each trip exceeds the volume limit. Constraint Eq. ( 14 ) restricts each distribution task on each trip to not exceeding the load limits. Constraint Eq. ( 15 ) limits the number of miles traveled by each carrier vehicle to no more than the vehicle's range limit. Constraint Eq. ( 16 ) restricts only vehicles that are in service to distribution duties. Constraint Eq. ( 17 ) restricts the order in which vehicles are put into service from m to m  + 1. The constraint Eqs. ( 18 – 20 ) defines the decision variable as 0 or 1.

Multi-objective processing

There are three objectives of different properties in the optimization model. Considering the complexity of the solution and the decision preference, this study uses the weighted sum method to integrate these objectives into a single objective function 30 . Setting the weights of the three objectives as convex combinations, i.e., \(\lambda_{1} ,\lambda_{2} ,\lambda_{3} \ge 0\) and \(\lambda_{1} + \lambda_{2} + \lambda_{3} = 1\) . Due to the large number of members in a distribution network and the fact that distribution costs are shared by all members, decision-making must take into account the opinions of all members. Group-analytic hierarchy process (G-AHP) is a comprehensive evaluation method developed based on hierarchical analysis, which can effectively integrate the knowledge and experience of multiple experts for group decision-making 31 . Therefore, this paper will apply G-AHP to determine the weights of the three objective functions, drawing on the reviews of a team of experts consisting of representatives from logistics centers and suppliers.

Calculate the weight vector of each expert's judgment matrix

In the formulation of the weight standard, a total of 5 experts participate in the group decision, and the judgment matrix constructed by the review opinion of each expert are \(A_{1} ,A_{2} ,A_{3} ,A_{4} ,A_{5}\) . The expression form of each judgment matrix is \(A_{l} = (a_{ijl} ); \, i,j = 1,2,3; \, l = 1,2, \ldots ,5\) , where \(a_{ijl}\) denotes the relative importance of factor i over factor j as perceived by the expert l . Separately solve their weight vectors \(\tilde{w}_{il} \, = \,(\,\tilde{w}_{1l} ,\tilde{w}_{2l} \,,\tilde{w}_{3l} \,)^{T}\) , where \(\tilde{w}_{il}\) represents the judgment weight value of the expert l for the objective function i . To ensure the consistency of the weight calculation results, a consistency check is performed, aiming for \(CR_{l} = CI_{l} /RI_{l} < 0.1\) .

Calculate the group’s composite weight vector

In this paper, considering the fairness and balance, under the condition that \(\sum {\lambda_{l} = 1,\quad (\lambda_{l} > 0,\quad l = 1,2, \ldots ,5)}\) , the weight of each review expert's opinion is set to be \(\lambda_{l} = 1/5 = 0.2\) . Performing the weighted arithmetic mean calculation on the respective components of each weight vector, as shown in Eq. ( 21 ).

Following normalization of \(\tilde{w}_{i}\) as Eq. ( 22 ), the weight vectors for the three objectives can be derived as \(w_{i} = [0.164,0.539,0.297]^{T}\) .

To address the dimensional differences among \(Z_{1}\) and \(Z_{2}\) , the method of min–max normalization is employed to transform the multi-objective problem into a scalar optimization problem. Where \(\underline{{Z_{1} }} ,\underline{{Z_{2} }} ,\underline{{Z_{3} }}\) are the lower bounds of \(Z_{1} ,Z_{2} ,Z_{3}\) , and \(\overline{{Z_{1} }} ,\overline{{Z_{2} }} ,\overline{{Z_{3} }}\) are the upper bounds of \(Z_{1} ,Z_{2} ,Z_{3}\) . The lower and upper bounds are calculated by the box constraint. The transformed single objective optimization model is shown in Eq. ( 23 ).

Solution methodology

Vehicle routing problem (VRP) is NP-hard problem, which is usually solved using heuristic algorithms 32 . Genetic algorithm searches for optimal solutions by simulating the natural selection and genetics mechanism of Darwin's biological evolution theory, which has a strong global search ability and can achieve desirable optimization effects in solving VRP. However, because it is a stochastic search method, the local search ability is insufficient. Therefore, in this paper, a hybrid adaptive genetic algorithm (HAGA) is designed to enhance the solution quality and robustness of the solution algorithm by improving the genetic algorithm on initialization population, genetic strategy, and local search, respectively, with respect to the problem characteristics. For "the bin packing problem" generated by the multi-trip distribution of vehicles, this paper proposes a bin packing algorithm based on transfer-of-state equation to solve the problem.

• Hybrid adaptive genetic algorithm

Coding method

Encoding is the process of mapping the solution space of a problem to a genotype representation in a genetic algorithm, allowing the algorithm to manipulate and evolve individuals. To increase the speed of model solving, this paper uses integer encoding.

Initialize population based on NNC rule

The classical genetic algorithm generates the initial population using a randomized method, resulting in a low degree of individual adaptation, which restricts the convergence speed of the algorithm. The nearest neighbor rule based on minimum cost (NNC) is a construction rule that produces higher quality feasible solutions, an idea first proposed by Solomon 33 . In this paper, we use the NNC rule to optimize the initial individuals and leverage its local optimization search to generate new individuals, aiming to improve the overall quality of the initialized population and accelerate the optimization process.

The “distance” between sites is defined as a weighted sum of the travel time between the two sites, the proximity of the time windows, and the urgency of the time window at the latter site. The “proximity of the time window” is the difference between the “start of service” at the latter site and the “completion of service” at the former site. The “urgency of the time window” of a site is the difference between the “latest service time” of the site and the “start service time” of the site. The “distance” between the two sites is shown in Eq. ( 24 ).

where c ij represents the "distance" between sites i and j . t ij represents the travel time from site i to j . T ij represents the proximity of the time window of sites i and j . E ij represents the urgency of the time window of site j . δ 1 , δ 2 , δ 3 represent the weighting coefficients, which satisfy δ 1  +  δ 2  +  δ 3  = 1.

Fitness function

The fitness function is used to measure the degree of adaptation of an individual in the problem space. The larger the value of the individual’s fitness, the higher the probability of remaining for the next generation of individual reproduction. For the minimization optimization model in this paper, the fitness is designed to be inversely proportional to the objective function, as shown in Eq. ( 25 ).

where Z denotes the objective function after transformation processing, as shown in Eq. ( 23 ).

Selection operation

The selection operator is a key step used to choose individuals for the next generation. Its main purpose is to select superior individuals from the current population based on their fitness values for subsequent operations. In this paper, tournament selection was used.

Adaptive scheme

The genetic algorithm is improved by using an adaptive genetic strategy, which adaptively changes the crossover and mutation probabilities according to individual fitness values, effectively avoiding local optima 34 . At the beginning of the genetic algorithm run, the individual differences are quite large. A higher crossover probability is chosen to increase the rate of new individual emergence, and a smaller mutation probability is chosen to speed up the convergence of results. In the later stages of the genetic algorithm, to reduce the probability of the algorithm falling into a local optimum, a smaller crossover probability should be used to protect well-adapted individuals, and a larger mutation probability should be used to increase the diversity of the population. In this way, the algorithm can jump out of the local optimum in time and determine the optimal or near-optimal solution in the shortest possible time. The following is the expression of crossover probability Eq. ( 26 ) and mutation probability Eq. ( 27 ) for the adaptive genetic operator proposed in this study.

where P c 1 is the set minimum crossover probability. P c 0 is the set maximum crossover probability. P m 1 is the set minimum mutation probability. P m 0 is the set maximum mutation probability. f avg is the current population mean fitness value. f max is the population maximum fitness value. f’ is the fitness value of the larger of the two individuals that crossover. f is the fitness value of the variant individual.

Cross operation

The crossover operator acts on the two paternal chromosomes to produce two new offspring individuals that contain the paternal genes but are different from the paternal chromosomes. In this paper, the OX crossover is used to speed up the operation and better preserve individual personality. The crossover operation process is shown in Fig.  5 .

OX cross operation.

Mutation operation

The mutation operator acts on one parent chromosome to make individuals in the population mutate, enriching the diversity of chromosomes within the population, improving the algorithm's ability to find the best, and preventing the algorithm from maturing prematurely. The mutation operation process is shown in Fig.  6 .

Mutation operation.

Local search operation

Large-scale neighborhood search algorithms (LNS) have the advantage of local search ability. Therefore, this paper leverages the core concepts of LNS, i.e., destruction and repair 35 , to design remove and reinserting operators to effectively compensate for the lack of local search ability of adaptive genetic algorithms. The remove operator refers to removing a portion of supplier sites from the current solution, while the reinserting operator refers to reinserting the removed supplier sites into the current solution. The schematic of local search is shown in Fig.  7 .

Schematic of the local search.

Remove operator

The removal operator is designed to identify distribution sites for removal based on the correlation value R . The removal operator operates as follows: Firstly, set the number p of sites to be removed, randomly select a site i from the current solution to be removed, and store the sites to be removed in the set S. Then calculate the correlation R between the remaining sites and the selected sites, and sort the correlations, select the site with the largest correlation for removal, and add it to the set S . The process is repeated until p - 1 destructive sites have been selected. The calculation of R is shown in Eq. ( 28 ).

where \(D_{ij}{\prime}\) denotes the normalized distance value between sites i and j , which is calculated as in Eq. ( 29 ). \(V_{ij}\) denotes whether site i and j are on the same routing or not, and is 1 if they are on the same routing, and 0 otherwise.

Reinserting operator

After removing a number of distribution sites using the remove operator, the removed distribution sites are then reinserted into the relevant locations on the routing using the reinserting operator, and the insertion is checked to see if the constraints are satisfied. The reinserting operator operates as follows: Firstly, find the best insertion position of each site in the set S that minimizes the increase of the objective function value in the post-destruction solution. Then calculate the target increase value of each site in S after inserting it to the best position, choose the site with the largest target increase value as the first insertion point, and repeat this operation until all sites in the set S are inserted into the destructed solution.

Bin packing algorithm based on transfer-of-state equation for solving multi-trip distribution

In the MTGVRPTW, which considers single-vehicle multi-trip delivery, the process of assigning trips to vehicles to obtain a solution is a typical "bin packing problem". The problem can be described as follows: there are a sufficient number of carrier vehicles \({\varvec{M}} = \{ 1,2 \ldots ,M^{*} \}\) , and the maximum operation time of the vehicle is \(W\) . The distribution routings set \({\varvec{K}} = \{ 1,2 \ldots ,K^{*} \}\) of several trips is obtained after decoding each chromosome of HAGA algorithm, and the distribution time of each trip is \(w_{k}\) . The task now is to design an algorithm that optimizes the allocation of distribution trips, aiming to minimize the number of vehicles required. The bin packing problem in this paper can be denoted by Eq. ( 30 – 34 ):

where Eq. ( 30 ) denotes that the objective of optimization is to use the minimum number of vehicles. Constraint Eq. ( 31 ) denotes that the total time of multi-trip distribution by each vehicle does not exceed the total working time of the sites. Constraint Eq. ( 32 ) indicates that all trips are carried by one and only one vehicle. Equations ( 32 , 34 ) defines the decision variable as 0 or 1.

In the solution of this problem, while the optimal solution can be obtained by using the exact solution method, the calculation process is more complicated. If the greedy algorithm is used, the results can be obtained faster, but the results are often not satisfactory. Therefore, this paper combines the exact solution method of dynamic programming with the greedy idea, and designs a combinatorial solution method based on transfer-of-state equation for solving the "bin packing problem" in the multi-trip distribution problem.

It is worth noting that the bin packing algorithm can effectively solve the multi-trip merging problem when all delivery sites share a unified time window. However, it may not be applicable when the time windows of different sites vary. That said, most manufacturing factories operate under a uniform work schedule, and reverse logistics deliveries are generally less urgent. Therefore, this method can be well-compatible within the industry.

The flowchart of the final solution algorithm for the problem model of this paper is shown in Fig.  8 .

The algorithm flowchart for solving the problem.

Computational experiments

In this section, we explore an application example scenario based on the MTGVRPTW model and the solution algorithm for the RT automotive scrap parts logistics center to assess the implementation benefits of the improved distribution model and the effectiveness of the solution algorithm. For those enterprises involved in more complex supply chains, which are located in industrial clusters with many suppliers, the distribution scale of reverse logistics terminals will be even larger. Therefore, this paper is oriented to medium and large-scale cases for simulation and analysis, aiming to make the research more universally applicable to the industry and to test the robustness of the algorithm, thereby deepening the significance of the research.

Numerical analysis of application example scenarios

To more intuitively reflect the superiority of the hybrid adaptive genetic algorithm (HAGA) designed in this paper, the adaptive genetic algorithm (AGA), genetic algorithm (GA), and hierarchical particle swarm algorithm (HPSO) are introduced to conduct the comparative experiments. The solution results are compared and analyzed, in terms of convergence characteristics and solution quality, to verify the robustness and optimality-seeking ability of HAGA.

Algorithmic parameter setting

In this paper, the parameters of the algorithms are set utilizing arithmetic tests and references to previous research experience, as shown in Table 3 . All the algorithms are implemented by MATLAB R2017a 36 programming, and the experimental results are output by running the software. The computer parameters are configured as Intel Core i5-12500H, 2.5 GHz, 16 GB RAM. The results of each algorithm are based on 20 runs.

Data collection and processing

Extract supplier site coordinate data

In previous studies, to collect the planar coordinates of the distribution sites, most of the previous researchers used the method of collecting the latitude and longitude data of the distribution sites from maps, and then converting the latitude and longitude coordinates to the planar rectangular coordinates by using various software such as MAPGIS 37 . In this paper, we argue that although the method above is feasible, it takes a long time to extract the coordinates when encountering large-scale practical cases. Therefore, this paper proposes a fast extraction method of planar coordinates based on image recognition technology that is feasible on a small scale.

The idea of the method is that when the distribution area under study is small, the idea of mathematical differentiation can be applied to approximate the sphere as a plane. The distribution area of this paper is 28 km × 24 km. The schematic diagram of this method is shown in Fig.  9 . The specific operational procedure is as follows:

Data preparation and original layer. Initial data collection is performed using the Google Maps service to capture a planimetric map of the area containing all suppliers and set it as Layer 1. The image must contain a clear scale, as it is a key element for precise distance calculations.

Creation and annotation of the calculation layer. Create a new transparent layer, Layer 2, above Layer 1. In Layer 2, use a graphical marking tool (e.g., dots) to accurately annotate the scale and supplier site locations, optimizing the target capture efficiency of the image recognition algorithm. This step aims to avoid loss or noise interference in subsequent data processing.

Image grayscale processing. Remove Layer 1. Convert the image of Layer 2 to grayscale to reduce image complexity and decrease the computational demand of the algorithm, thereby improving the accuracy of the image recognition process.

Image recognition and data extraction. Apply image recognition technology to identify specific grayscale points in the image, which represent the pixel locations of the distribution sites. Simultaneously, the algorithm identifies the pixel coordinates at both ends of the scale and calculates the pixel distance between the two ends, providing the necessary data support for coordinate conversion.

Coordinate conversion and calibration. Based on the pixel distance provided by the scale, perform a proportional linear transformation on the pixel coordinates of the distribution sites, converting them into actual plane calculation coordinates. This step ensures the accurate conversion from the image to actual geographic locations.

Computational coordinate extraction method based on image recognition. The Maps were captured on the Google Maps 38 platform ( https://www.google.com/maps ).

To verify the accuracy and reliability of the data collection method in this paper, the collected data are compared and analyzed with the measurement data from the Baidu Maps platform. Some of the comparison results are shown in Table 4 . The distance calculated by each site through the image recognition technology to extract the coordinates compared with the actual distance, the accuracy ranged from 95 to 99%, and the overall average accuracy of 98.88%. These results are within the acceptable range, proving the effectiveness of the method proposed in this paper.

The method has several advantages over traditional methods. The image recognition method significantly reduces the manual involvement in data collection. For large-scale data collection scenarios, this method can complete data extraction for an entire area within minutes, whereas traditional methods may take hours or even longer to achieve the same task. The time-saving advantage of the proposed method is particularly significant when facing the coordinate collection of multi-change scenarios.

In addition, in the case of natural disasters and other scenarios, emergency shelters are usually located in no fixed place, and it is more scientific and feasible to extract coordinates based on real-time remote sensing satellite images using image recognition technology in the distribution of emergency supplies.

Description of other data

The model of this paper and other data involved in the calculation of the elaboration of the description: (i) The generation of scrap parts is unpredictable, and the number of each time period varies. In order to optimize the allocation of resources, each delivery is made with a third-party company to reach a vehicle rental and driver temporary employment agreement, so this paper is not constrained by the number of vehicles and drivers. (ii) Considering the large number of suppliers, scrap parts belonging to the same supplier are loaded using standardized cargo units. Considering the road conditions within the city, this study focuses on leasing 6.8-m vans. The vehicle specification is 6800 mm × 2450 mm × 2600 mm, with double-layer palletizing, and a single truck with 24 cargo unit positions. The cargo unit required by each supplier for daily distribution can be calculated from the current data. (iii) Considering the realistic road conditions and other factors, the average speed of vehicle traveling was set to be 35 km/h. (iv) Preparation costs for departure is set to 50 yuan/trip. (v) In the actual distribution, the distribution site is not a straight path. To make the simulation closer to reality, we set a certain relaxation factor, which is calculated as follows: simulation distance = euclidean distance between coordinates × relaxation factor. After calculation, this paper takes the value of 1.6. (vi) Transportation cost per unit distance for distribution vehicles is 2 yuan/km. Conversion factor for carbon emissions and fuel consumption \(e_{0}\) is calculated as 0.34L of diesel fuel for 1 kg of carbon dioxide. Vehicles with a maximum load capacity \(Q_{K}\) of 8 tons. Fuel consumption per uni15t distance \(\rho_{ok}\) at no load is 0.117 L/km. Fuel consumption per unit distance \(\rho_{k}^{*}\) at full load is 0.377 L/km. (vii) The normal working time window for the logistics center and each supplier is 9:00 to 17:00, which is unified.

Practical case solving

In the new distribution mode, the number of automotive scrap parts faced at different delivery cycle intervals varies, requiring different transportation resources. In the original mode, the average shipping frequency of point-to-point delivery is about 4–5 days. One of the purposes of this study is to reduce the operational pressure of the logistics center and reduce the inventory level, so the delivery cycle interval is set to 1–3 days, respectively. To help enterprises better reach cost reduction and efficiency, this paper analyzes the target benefits under different distribution cycle intervals. Some of the example data are shown in Table 5 .

The model solution results and algorithm iteration process under different distribution cycle intervals are shown in Fig.  10 and Table 6 .

The optimal routing of the solution and the iterative process of the algorithm.

Result discussion

Discussion of results using different optimization algorithms.

From Table 6 , it can be seen that there are significant differences in the optimization effects of different solving algorithms. In terms of the performance of the overall objective function, HAGA > AGA > GA ≈ HPSO, and the optimized number of trips and the number of vehicles after solving the HAGA is less than those of the AGA, GA, and HPSO, regardless of the distribution cycle intervals. In terms of the performance of the objective function \(Z_{1}\) , the HAGA is in the leading position, and its optimized carbon dioxide emissions are better than the AGA by an average of 7.46%, the GA by an average of 8.38%, and the HPSO by an average of 6.68% in different distribution cycle intervals. In terms of the performance of the objective function \(Z_{2}\) , the HAGA is also in the leading position, and its optimized overall logistics cost is better than the AGA by an average of 3.64%, the GA by an average of 7.75%, and the HPSO by an average of 7.94% in different distribution cycle intervals. Meanwhile, in terms of convergence speed, the HAGA can enter convergence with fewer iterations, and outperforms the AGA by an average of 16.83%, the GA by an average of 20.27%, and the HPSO by an average of 20.85% in different distribution cycle intervals. Although the solution time of the HAGA is slightly longer than the other algorithms, its computation time of only a few minutes does not overburden the overall task and is within an acceptable range.

Combined with Fig.  9 , HAGA initializes the population with better quality under the NNC rule, which seizes a head start for the subsequent optimality search. From the iterative curve, HAGA and AGA are better than GA and HPSO, which shows that the use of adaptive genetic strategy can help to maintain the diversity of the population and prevent the algorithm from converging to the local optimal solution too early. The superior search quality of HAGA compared to AGA shows that the global destroy-repair mechanism of the LNS algorithm can enhance the algorithm's ability of local search, and to a certain extent, prevent the algorithm from falling into local optimums. In addition, in terms of solution stability, after 20 runs, the percentage deviation of HAGA in the final objective function result is only 1.4%, while for the other algorithms is more than 15%, which sufficiently demonstrates that the solution of HAGA has stronger stability.

Discussion of the results of the strategy using different distribution cycle intervals

The solution results for different cycle intervals are not directly comparable, so in this paper, the magnitude is transformed to be on the unit of the month. The model solution results using the HAGA are shown in Table 7 after the comparability transformation.

The general trend of the different cycle intervals of distribution in various indicators is that the smaller the cycle interval, the higher the carbon dioxide emissions during distribution, the higher the overall logistics cost, but the better the improvement in inventory levels. Considering the current situation of the RT logistics center, a 31% reduction in the inventory level can sufficiently alleviate the current operational pressure. Therefore, the decision was made to adopt a distribution plan with a 3-day distribution cycle interval. In the future, according to the actual situation of the logistics center flexibly change the distribution cycle interval, in order to adapt to the needs of the development promptly. The specific distribution program is shown in Table 8 .

In addition, the adoption of a circular distribution model with a 3-day interval between delivery cycles has resulted in a 66.78% reduction in overall logistics cost, an 18.08% reduction in carbon dioxide emissions, and a 31% reduction in inventory levels compared to the initial point-to-point bulk delivery model, which is a significant improvement.

Simulation analysis under medium and large scale arithmetic examples

For those enterprises with more complex supply chains, they are located in industrial clusters with a large number of suppliers, and the scale of distribution at the reverse logistics terminal will also be larger. Therefore, this paper conducts extended experiments for medium and large-scale cases to make this study more universal for industry. At the same time, the robustness of the HAGA designed in this paper is examined using different example data to deepen the significance of the study.

Description of the simulation example

The randomized generation method is used to generate the medium and large-scale example data with the number of distribution sites of 40, 50, 60, and 70, respectively. The coordinates of each distribution site are randomly generated from 0 to 50 km, the number of cargo units required is randomly generated from 1 to 8, and the weight of materials is randomly generated from 200 to 2000 kg. The rest of the parameter settings are consistent with those in the previous section.

Simulation example solving

The solution is solved for different size cases and the results are shown in Table 9 .

Table 9 shows that the HAGA continues to outperform the other algorithms at all scales. It outperforms the other algorithms in terms of the total objective function \(Z\) , the number of trips, and the number of vehicles transported, which is consistent with the conclusions drawn in “ Result discussion ” section. In terms of the objective function \(Z_{1}\) , the HAGA outperforms the AGA by an average of 17.35%, the GA by an average of 25.41%, and the HPSO by an average of 20.79%. In terms of the performance of the objective function \(Z_{2}\) , the HAGA outperforms the AGA by 18.29% on average, the GA by 22.59% on average, and the HPSO by 21.67% on average. In addition, as the size of the arithmetic cases increases, HAGA has a greater advantage over the rest of the algorithms, proving that it possesses strong robustness. Meanwhile, in terms of convergence speed, the HAGA is able to reach the optimization with fewer iterations. However, in terms of solution time, HAGA is at a disadvantage compared to the other algorithms.

Focusing on the strategy of sustainable development, this paper studies the problem of green distribution of automobile scrap reverse logistics for industrial cluster areas. The main conclusions are as follows:

Under the comprehensive consideration of reducing the inventory level and realizing the cost reduction and efficiency improvement of logistics, a circular distribution mode based on Milk-run is proposed to replace the initial point-to-point batch distribution mode. To achieve the coordination between sustainable development strategy and enterprise cost-effectiveness, this paper introduces multiple optimization objectives of minimizing carbon dioxide emissions, overall logistics cost, and delayed delivery, constructs the MTGVRPTW model, and verifies the usability of the model.

Given the characteristics of this research problem, a hybrid adaptive genetic algorithm that combines the nearest neighbor rule based on minimum cost, adaptive strategy, bin-packing algorithm based on the transfer-of-state equation, large-scale neighborhood search algorithm and genetic algorithm, and the design process of the algorithm is described in detail. The robustness and stability of the HAGA algorithm are verified by the numerical calculation.

To efficiently obtain the location data of supplier factory sites in the distribution network, a coordinate extraction method based on image recognition technology is proposed. Validation results indicate that this method achieves an overall average accuracy of 98.88% in coordinate extraction, characterized by high efficiency and accuracy.

A case study was conducted on the RT logistics center to analyze its operational performance under different distribution strategies. The results indicate that shortening the distribution cycle interval significantly reduces inventory levels but also increases logistics costs and carbon dioxide emissions. The analysis concluded that adopting a three-day cycle distribution model better meets the current development needs of the RT logistics center. Compared to the initial point-to-point batch distribution model, overall logistics cost decreased by 66.78%, carbon dioxide emissions reduced by 18.08%, and inventory levels dropped by 31%, demonstrating significant improvements. In addition, by introducing medium-to-large-scale simulation examples, the significant advantages of the HAGA algorithm over other algorithms in terms of robustness and optimization ability are verified. It also shows that the research results have good application universality in similar industries.

Data availability

Some of the data used in this study are presented in the manuscript, and the remaining data are available upon reasonable request from the corresponding author. The complete data are not directly disclosed because they may compromise the privacy of the study participants.

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This work was supported in part by the Natural Science Foundation of Heilongjiang Province, Grant No. LH2023G001.

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Wang, H., Hao, H. & Wang, M. Optimization research on multi-trip distribution of reverse logistics terminal for automobile scrap parts under the background of sustainable development strategy. Sci Rep 14 , 17305 (2024). https://doi.org/10.1038/s41598-024-68112-4

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Investigation of bus shelters and their thermal environment in hot–humid areas—a case study in guangzhou.

1. Introduction

2. methodology, 2.1. bus shelters investigation, 2.1.1. research objects, 2.1.2. investigation method, 2.2. thermal environment measurement of bus shelter, 2.3. measurement data analysis, 2.4. investigation of cooling behavior of people in waiting area, 3.1. basic information of bus shelters, 3.1.1. street view date and bus shelter orientation, 3.1.2. combined bus shelter and station board, 3.1.3. number of bus shelter roofs, 3.1.4. types of bus shelter roof and underlying surface, 3.2. typical style of bus shelter, 3.3. thermal environment of bus shelter, 3.4. cooling behavior of people under bus shelter, 4. discussion, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Pan, Y.; Li, S.; Tang, X. Investigation of Bus Shelters and Their Thermal Environment in Hot–Humid Areas—A Case Study in Guangzhou. Buildings 2024 , 14 , 2377. https://doi.org/10.3390/buildings14082377

Pan Y, Li S, Tang X. Investigation of Bus Shelters and Their Thermal Environment in Hot–Humid Areas—A Case Study in Guangzhou. Buildings . 2024; 14(8):2377. https://doi.org/10.3390/buildings14082377

Pan, Yan, Shan Li, and Xiaoxiang Tang. 2024. "Investigation of Bus Shelters and Their Thermal Environment in Hot–Humid Areas—A Case Study in Guangzhou" Buildings 14, no. 8: 2377. https://doi.org/10.3390/buildings14082377

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