real life case study on transportation problem

• City by city
• Current issues
• General election 2024
• Metro mayors
• Levelling up
• Productivity

Transport Case Studies

How different cities are improving their transport connections using our case study library

Case study library

Why improve transport?

Effective transport is vital for the economy. Good transport connections have direct benefits to people, businesses, the environment, and the economy overall. They can support innovation, help people access jobs, shape greener and healthier places, and attract new firms.

Having more control over transport can help cities make the most of their infrastructure by reducing uncertainty and short timescale of funding, improving the bus system, and ensuring integration of transport, economic development and infrastructure.

Use the table below to find out more about how and what cities in the UK and abroad have done to improve transport. These case studies highlight what the cities did and their self-reported outcomes.

To find out more about what types of transport what types of policy interventions have an impact on local economic growth from across OECD countries, as well as evidence-based policy design guides, take a look at the What Works Centre pages on Transport.

Case studies

click the case study title to find out more

More Case Studies on Housing and Transport

Delivering change: making transport work for cities.

Zach Wilcox and Nada Nohrová

Delivering change: Putting city centres at the heart of the local economy

Louise McGough and Elli Thomas

Open Data or Closed Doors?

Maire Williams

Funding and financing inclusive growth in cities

Naomi Clayton, Simon Jeffrey and Anthony Breach

How can UK cities clean up the air we breathe?

Adeline Bailly

Policy Officer

More on this issue

Anthony Breach

The first 100 days

Paul Swinney

James Evans

Combined authorities

• Browse All Articles
• Newsletter Sign-Up

Transportation →

• 21 May 2024
• Cold Call Podcast

The Importance of Trust for Managing through a Crisis

In March 2020, Twiddy & Company, a family-owned vacation rental company known for hospitality rooted in personal interactions, needed to adjust to contactless, remote customer service. With the upcoming vacation season thrown into chaos, President Clark Twiddy had a responsibility to the company’s network of homeowners who rented their homes through the company, to guests who had booked vacations, and to employees who had been recruited by Twiddy’s reputation for treating staff well. Who, if anyone, could he afford to make whole and keep happy? Harvard Business School professor Sandra Sucher, author of the book The Power of Trust: How Companies Build It, Lose It, Regain It, discusses how Twiddy leaned into trust to weather the COVID-19 pandemic in her case, “Twiddy & Company: Trust in a Chaotic Environment.”

• 15 May 2024
• Research & Ideas

A Major Roadblock for Autonomous Cars: Motorists Believe They Drive Better

With all the advances in autonomous vehicle technology, why aren't self-driving cars chauffeuring more people around? Research by Julian De Freitas, Stuti Agarwal, and colleagues reveals a simple psychological barrier: Drivers are overconfident about their own abilities, so they resist handing over the wheel.

• 28 Apr 2023

Sweden’s Northvolt Electric Battery Maker: A Startup with a Mission

In Stockholm, Sweden an upstart battery maker, Northvolt, is trying to recreate the value chain for European car manufacturers making the switch to EVs. With two founders from Tesla and two experienced financiers at the helm, the company seems bound for success. But can they partner with government, scale fast enough, and truly be part of the climate solution? Harvard Business School professor George Serafeim discusses what it takes to scale a business—the right people, in the right place, at the right time—with the aim of providing a climate solution in the case, “Northvolt, Building Batteries to Fight Climate Change.” As part of a new first-year MBA course at Harvard Business School, this case examines the central question: what is the social purpose of the firm?

• 11 Apr 2023

A Rose by Any Other Name: Supply Chains and Carbon Emissions in the Flower Industry

Headquartered in Kitengela, Kenya, Sian Flowers exports roses to Europe. Because cut flowers have a limited shelf life and consumers want them to retain their appearance for as long as possible, Sian and its distributors used international air cargo to transport them to Amsterdam, where they were sold at auction and trucked to markets across Europe. But when the Covid-19 pandemic caused huge increases in shipping costs, Sian launched experiments to ship roses by ocean using refrigerated containers. The company reduced its costs and cut its carbon emissions, but is a flower that travels halfway around the world truly a “low-carbon rose”? Harvard Business School professors Willy Shih and Mike Toffel debate these questions and more in their case, “Sian Flowers: Fresher by Sea?”

• 28 Mar 2023

BMW’s Decarbonization Strategy: Sustainable for the Environment and the Bottom Line

In mid-2022, automakers, consumers, regulators, and investors were focusing on the transition from internal combustion engine (ICE) vehicles to electric vehicles (EV). While this would reduce tail-pipe emissions, it ignored the fact that the production of EVs—and especially their batteries—increases emissions in the supply chain. Many automakers were announcing deadlines by which they would stop selling ICE vehicles altogether, buoyed by investment analysts and favorable press. But BMW decided to focus on lifecycle emissions and pursued a flexible powertrain strategy by offering vehicles with several options: gasoline and diesel-fueled ICE, plug-in hybrid electric vehicles, and battery electric vehicles. That approach received a frostier reception in the stock market. Assistant Professor Shirley Lu discusses how BMW plans to convince stakeholders that its strategy is good for both the environment and the company’s financial performance in the case, “Driving Decarbonization at BMW.”

• 05 Jul 2022
• What Do You Think?

Have We Seen the Peak of Just-in-Time Inventory Management?

Toyota and other companies have harnessed just-in-time inventory management to cut logistics costs and boost service. That is, until COVID-19 roiled global supply chains. Will we ever get back to the days of tighter inventory control? asks James Heskett. Open for comment; 0 Comments.

• 17 Aug 2021

Can Autonomous Vehicles Drive with Common Sense?

Driverless vehicles could improve global health as much as the introduction of penicillin. But consumers won't trust the cars until they behave more like humans, argues Julian De Freitas. Open for comment; 0 Comments.

• 11 Jan 2021
• Working Paper Summaries

Accounting for Product Impact in the Airlines Industry

A systematic methodology for measuring product impact can be applied across a range of industries. Examining two competitor companies in the airlines industry, this study finds that analyzing each dimension of product impact allows for deeper understanding of each company’s business strategies.

• 02 Feb 2016

Commuting with a Plan: How Goal-Directed Prospection Can Offset the Strain of Commuting

Employees often say commuting is the least desirable time period of the day. Those who use the time to think about the future in terms of goals to pursue can turn the daily hassle of commuting into something useful and meaningful. While to some extent commuting time may be outside employees’ control, they are nonetheless in charge of their commute.

• 20 Jan 2016

Maybe Uber isn't God's Gift to Mankind

Benjamin G. Edelman discusses the potential negative effects of transportation network companies in the so-called sharing economy. Open for comment; 0 Comments.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

The problem of sustainable intermodal transportation: a case study of an international logistics company, turkey.

1. Introduction

2. materials and methods, 2.1. problem.

• Roro + Train: For export operations, transportation is from Turkey ports to the Trieste port with Roro and then to Ostrava by train.
• Roro + Truck: For export operations, transportation is from Turkey ports to the Trieste port with Roro and then to Ostrava, Bratislava, and Poland with trucks.
• Truck: Transport is from Turkey to Ostrava, Bratislava, and Poland with trucks.
• Minimization of the total transportation costs based on risk weights.

2.2. The Mathematical Model Approach

2.3. method, the fuzzy approach, 3. a real-life application for an international logistics company, 5. discussion, 6. conclusions, author contributions, acknowledgments, conflicts of interest.

• Oudani, M.; Alaoui, A.E.H.; Boukachour, J. An efficient genetic algorithm to solve the intermodal terminal location problem. Int. J. Sup. Oper. Man. 2014 , 1 , 279–296. [ Google Scholar ]
• World Health Organization (WHO). Global Status Report on Road Safety. Available online: http://www.who.int/violence_injury_prevention/road_safety_status/report/en/ (accessed on 10 September 2018).
• Past Energy Related Emissions from the Climate Change Performance Index (CCPI). Past Non-Energy and Future Emissions Projections from the Climate Action Tracker (CAT). Available online: https://germanwatch.org/sites/germanwatch.org/files/publication/15999.pdf (accessed on 9 September 2018).
• Lopez-Navarro, M.A. Environmental factors and intermodal freight transportation: Analysis of the decision bases in the case of Spanish motorways of the sea. Sustainability 2014 , 6 , 1544–1566. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Jugovic, A.; Debelic, B.; Brdar, M. Short sea shipping in Europe factor of the sustainable development transport system of Croatia. Sci. J. Marit. Res. 2011 , 25 , 109–124. [ Google Scholar ]
• Janic, M. Modelling the full costs of an intermodal and road freight transport network. Trans. Res. D 2007 , 12 , 33–44. [ Google Scholar ] [ CrossRef ]
• Mostert, M.; Limbourg, S. External costs as competitiveness factors for freight transport—A state of the art. Trans. Rev. 2016 , 36 , 692–712. [ Google Scholar ] [ CrossRef ]
• Janic, M. An assessment of the performance of the European long intermodal freight trains (LIFTS). Trans. Res. A 2008 , 42 , 1326–1339. [ Google Scholar ] [ CrossRef ]
• Janic, M.; Vleugel, J. Estimating potential reductions in externalities from rail-road substitution in Trans-European freight transport corridors. Trans. Res. D 2012 , 17 , 154–160. [ Google Scholar ] [ CrossRef ]
• Gennaro, M.C.; Paffumi, E.; Martini, G. Big data for supporting low-carbon road transport policies in Europe: Applications, challenges and opportunities. Big Data Res. 2016 , 6 , 11–25. [ Google Scholar ] [ CrossRef ]
• Sgouris, S.; Bonnefoy, P.A.; Hansman, R.J. Air transportation in a carbon constrained world: Long term dynamics of policies and strategies for mitigating the carbon footprint of commercial aviation. Trans. Res. A 2011 , 45 , 1077–1091. [ Google Scholar ]
• Li, Y.; Bao, L.; Li, W.; Deng, H. Inventory and policy reduction potential of greenhouse gas and pollutant emissions of road transportation industry in china. Sustainability 2016 , 8 , 1218. [ Google Scholar ] [ CrossRef ]
• World Health Organization (WHO). Global Status Report on Road Safety 2013: Supporting a Decade of Action (Official Report) ; WHO: Geneva, Switzerland, 2013. [ Google Scholar ]
• Tuzkaya, U.R. Evaluating the environmental effects of transportation modes using an integrated methodology and an application. Int. J. Environ. Sci. Technol. 2009 , 6 , 277–290. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Min, H. International intermodal choices via chance constrained goal programming. Trans. Res. A 1991 , 25 , 351–362. [ Google Scholar ] [ CrossRef ]
• Choong, S.T.; Cole, M.H.; Kutanoglu, E. Empty container management for intermodal transportation networks. Trans. Res. Part E Logist. Transp. Rev. 2002 , 38 , 423–438. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Chang, T.S. Best routes selection in international intermodal networks. Comput. Oper. Res. 2008 , 35 , 2877–2891. [ Google Scholar ] [ CrossRef ]
• Bauer, J.; Bektaş, Y.; Crainic, T.G. Minimizing greenhouse gas emissions in intermodal freight transport: An application to rail service design. J. Oper. Res. Soc. 2010 , 61 , 530–542. [ Google Scholar ] [ CrossRef ]
• Verma, M.; Verter, V.; Zufferey, N. A bi-objective model for planning and managing rail-truck intermodal transportation of hazardous materials. Trans. Res. Part E 2012 , 48 , 132–149. [ Google Scholar ] [ CrossRef ]
• Tierney, K.; Voß, S.; Stahlbock, R. A mathematical model of inter-terminal transportation. Eur. J. Oper. Res. 2014 , 235 , 448–460. [ Google Scholar ] [ CrossRef ]
• Serper, E.Z.; Alumur, S.A. The design of capacitated intermodal hub networks with different vehicle types. Trans. Res. Part B 2016 , 86 , 51–65. [ Google Scholar ] [ CrossRef ]
• Baykasoğlu, A.; Subulan, K. A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application. Trans. Res. Part E 2016 , 95 , 207–247. [ Google Scholar ] [ CrossRef ]
• Tian, W.L.; Cao, C.X. A generalized interval fuzzy mixed integer programming model for a multimodal transportation problem under uncertainty. Eng. Optim. 2017 , 49 , 481–498. [ Google Scholar ] [ CrossRef ]
• Xie, Y.C.; Lu, W.; Wang, W.; Quadrifoglio, L. A multimodal location and routing model for hazardous materials transportation. J. Hazard. Mater. 2012 , 227–228 , 135–141. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kunadhamraks, P.; Hanaoka, S. Evaluating the logistics performance of intermodal transportation in Thailand. Asia Pac. J. Mark. Logist. 2008 , 20 , 323–342. [ Google Scholar ] [ CrossRef ]
• Qu, L.; Chen, Y. A hybrid MCDM Method for Route Selection of Multimodal Transportation Network. Adv. Neural Netw. 2008 , 5263 , 374–383. [ Google Scholar ]
• Moon, D.S.; Kim, D.J.; Lee, E.K. A study on Competitiveness of Sea Transport by Comparing International Transport Routes between Korea and EU. Asian J. Shipp. Logist. 2015 , 31 , 1–20. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; Yeo, G.T. A Study on International Multimodal Transport Networks from Korea to Central Asia: Focus on Secondhand Vehicles. Asian J. Shipp. Logist. 2016 , 32 , 41–47. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; Yeo, G.T. Intermodal route selection for cargo transportation from Korea to Central Asia by adopting Fuzzy Delphi and Fuzzy ELECTRE I methods. Marit. Policy Manag. 2018 , 45 , 3–18. [ Google Scholar ] [ CrossRef ]
• Banyai, T.; Illes, B.; Banyai, A. Smart scheduling: An integrated first mile and last mile supply approach. Complexity 2018 , 2018 , 5180156. [ Google Scholar ] [ CrossRef ]
• Banyai, T. Real-time decision making in first mile and last mile logistics: How smart scheduling affects energy efficiency of hyperconnected supply chain solutions. Energies 2018 , 11 , 1833. [ Google Scholar ] [ CrossRef ]
• Yuan, B.; Gu, B.; Guo, J.; Xia, L.; Xu, C. The optimal decisions for a sustainable supply chain with carbon information asymmetry under cap-and-trade. Sustainability 2018 , 10 , 1002. [ Google Scholar ] [ CrossRef ]
• Brandenburg, M. Low carbon supply chain configuration for a new product—A goal programming approach. Int. J. Prod. Res. 2015 , 53 , 6588–6610. [ Google Scholar ] [ CrossRef ]
• Mari, S.I.; Lee, Y.H.; Memon, M.S. Sustainable and resilient supply chain network design under disruption risks. Sustainability 2014 , 6 , 6666–6686. [ Google Scholar ] [ CrossRef ]
• Glickman, T.S.; Erkut, E.; Zschocke, M.S. The cost and risk impacts of rerouting railroad shipments of hazardous materials. Accid. Anal. Prev. 2007 , 39 , 1015–1025. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Bubbico, R.; Maschio, G.; Mazzarotta, B.; Milazzo, M.F.; Parisi, E. Risk management of road and rail transport of hazardous materials in Sicily. J. Loss Prev. Proc. Ind. 2006 , 19 , 32–38. [ Google Scholar ] [ CrossRef ]
• Verma, M.; Verter, V. A lead-time based approach for planning rail–truck intermodal transportation of dangerous goods. Eur. J. Oper. Res. 2010 , 202 , 696–706. [ Google Scholar ] [ CrossRef ]
• Raemdonck, K.V.; Macharis, C.; Mairesse, O. Risk analysis system for the transport of hazardous materials. J. Saf. Res. 2013 , 45 , 55–63. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fabiano, B.; Currò, F.; Palazzi, E.; Pastorino, R. A framework for risk assessment and decision-making strategies in dangerous good transportation. J. Hazard. Mater. 2002 , 93 , 1–15. [ Google Scholar ] [ CrossRef ]
• Huang, B.; Cheu, R.L.; Liew, Y.S. GIS and genetic algorithms for HAZMAT route planning with security considerations. Int. J. Geogr. Inf. Sci. 2004 , 18 , 769–787. [ Google Scholar ] [ CrossRef ]
• Guo, X.; Verma, M. Choosing vehicle capacity to minimize risk for transporting flammable materials. J. Loss Prev. Proc. Ind. 2010 , 23 , 220–225. [ Google Scholar ] [ CrossRef ]
• Yang, J.; Li, F.; Zhou, J.; Zhang, L.; Huang, L.; Bi, J. A survey on hazardous materials accidents during road transport in China from 2000 to 2008. J. Hazard. Mater. 2010 , 184 , 647–653. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Winslott Hiselius, L. Using choice experiments to assess people’s preferences for railway transports of hazardous materials. Risk Anal. 2005 , 25 , 1199–1214. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Verma, M. A cost and expected consequence approach to planning and managing railroad transportation of hazardous materials. Trans. Res. Part D 2009 , 14 , 300–308. [ Google Scholar ] [ CrossRef ]
• Reilly, A.; Nozick, L.; Xu, N.; Jones, D. Game theory-based identification of facility use restrictions for the movement of hazardous materials under terrorist threat. Trans. Res. Part E 2012 , 48 , 115–131. [ Google Scholar ] [ CrossRef ]
• Liu, X.; Saat, M.R.; Barkan, C.P.L. Probability analysis of multiple tank-car release incidents in railway hazardous materials transportation. J. Hazard. Mater. 2014 , 276 , 442–451. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Reniers, G.L.L.; Dullaert, W. A method to assess multi-modal Hazmat transport security vulnerabilities: Hazmat transport SVA. Trans. Policy 2013 , 28 , 103–113. [ Google Scholar ] [ CrossRef ]
• Islam, D.M.Z.; Ricci, S.; Nelldal, B.L. How to make modal shift from road to rail possible in the European transport market, as aspired to in the EU Transport White Paper 2011. Eur. Trans. Res. Rev. 2016 , 8 , 18. [ Google Scholar ] [ CrossRef ]
• Hanaoka, S.; Kunadhamraks, P. Multiple criteria and fuzzy based evaluation of logistics performance for intermodal transportation. J. Adv. Trans. 2009 , 43 , 123–153. [ Google Scholar ] [ CrossRef ]
• Tuzkaya, U.R.; Onut, S. A fuzzy analytic network process based approach to transportation-mode selection between Turkey and Germany: A case study. Inf. Sci. 2008 , 178 , 3133–3146. [ Google Scholar ] [ CrossRef ]
• Sterzik, S.; Kopfer, H. A tabu search heuristic for the inland container transportation problem. Comput. Oper. Res. 2013 , 40 , 953–962. [ Google Scholar ] [ CrossRef ]
• Ekol Logistics. Available online: https://www.ekol.com/en/services/freight/intermodal/routes/ (accessed on 1 September 2018).

Click here to enlarge figure

Share and Cite

Göçmen, E.; Erol, R. The Problem of Sustainable Intermodal Transportation: A Case Study of an International Logistics Company, Turkey. Sustainability 2018 , 10 , 4268. https://doi.org/10.3390/su10114268

Göçmen E, Erol R. The Problem of Sustainable Intermodal Transportation: A Case Study of an International Logistics Company, Turkey. Sustainability . 2018; 10(11):4268. https://doi.org/10.3390/su10114268

Göçmen, Elifcan, and Rızvan Erol. 2018. "The Problem of Sustainable Intermodal Transportation: A Case Study of an International Logistics Company, Turkey" Sustainability 10, no. 11: 4268. https://doi.org/10.3390/su10114268

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

• Open access
• Published: 20 July 2023

Solving a multi-objective solid transportation problem: a comparative study of alternative methods for decision-making

• Mohamed H. Abdelati   ORCID: orcid.org/0000-0002-5034-7323 1 ,
• Ali M. Abd-El-Tawwab 1 ,
• Elsayed Elsayed M. Ellimony 2 &
• M Rabie 1  

Journal of Engineering and Applied Science volume  70 , Article number:  82 ( 2023 ) Cite this article

2962 Accesses

1 Citations

Metrics details

The transportation problem in operations research aims to minimize costs by optimizing the allocation of goods from multiple sources to destinations, considering supply, demand, and transportation constraints. This paper applies the multi-dimensional solid transportation problem approach to a private sector company in Egypt, aiming to determine the ideal allocation of their truck fleet.

In order to provide decision-makers with a comprehensive set of options to reduce fuel consumption costs during transportation or minimize total transportation time, a multi-objective approach is employed. The study explores the best compromise solution by leveraging three multi-objective approaches: the Zimmermann Programming Technique, Global Criteria Method, and Minimum Distance Method. Optimal solutions are derived for time and fuel consumption objectives, offering decision-makers a broad range to make informed decisions for the company and the flexibility to adapt them as needed.

Lingo codes are developed to facilitate the identification of the best compromise solution using different methods. Furthermore, non-dominated extreme points are established based on the weights assigned to the different objectives. This approach expands the potential ranges for enhancing the transfer problem, yielding more comprehensive solutions.

This research contributes to the field by addressing the transportation problem practically and applying a multi-objective approach to support decision-making. The findings provide valuable insights for optimizing the distribution of the truck fleet, reducing fuel consumption costs, and improving overall transportation efficiency.

Introduction

The field of operations research has identified the transportation problem as an optimization issue of significant interest [ 1 , 2 ]. This problem concerns determining the optimal approach to allocate a given set of goods that come from particular sources to the designated destinations to minimize the overall transportation costs [ 3 ]. The transportation problem finds applications in various areas, including logistics planning, distribution network design, and supply chain management. Solving this problem relies on the assumption that the supply and demand of goods are known, as well as the transportation cost for each source–destination pairing [ 4 , 5 ].

Solving the transportation problem means finding the right quantities of goods to be transported from the sources to the destinations, given the supply and demand restrictions. The ultimate goal is to minimize the total transportation cost, which is the sum of the cost for each shipment [ 6 ]. Various optimization algorithms have been developed for this problem, such as the North-West Corner Method, the Least Cost Method, and Vogel’s Approximation Method [ 7 ].

A solid transportation problem (STP) is a related transportation problem that centers around a single commodity, which can be stored at interim points [ 8 ]. These interim points, known as transshipment points, act as origins and destinations. The STP involves determining the most efficient means of transporting the commodity from the sources to the destinations, while minimizing transportation costs by going through the transshipment points. The STP has real-world applications in container shipping, air cargo transportation, and oil and gas pipeline transportation [ 9 , 10 ].

Multi-dimensional solid transportation problem (MDSTP) represents a variation on the STP, incorporating multiple commodities that vary in properties such as volume, weight, and hazard level [ 11 ]. The MDSTP aims to identify the best way to transport each commodity from the sources to the destinations, taking into account the capacity restrictions of transshipment points and hazardous commodity regulations [ 12 ]. The MDSTP is more complex than the STP and requires specific algorithms and models for its resolution.

Solving the STP and MDSTP requires identifying the most effective routing of commodities and considering the storage capacity of transshipment points. The goal is to minimize total transportation costs while satisfying supply and demand constraints and hazardous material regulations. Solutions to these problems include the Network Simplex Method, Branch and Bound Method, and Genetic Algorithm [ 13 ]. Solving the STP and MDSTP contributes valuable insights into the design and operation of transportation systems and supports improved sustainability and efficiency.

In the field of operations research, two critical research areas are the multi-objective transportation problem (MOTP) and the multi-objective solid transportation problem (MOSTP) [ 14 ]. The MOTP aims to optimize the transportation of goods from multiple sources to various destinations by considering multiple objectives, including minimizing cost, transportation time, and environmental impacts. The MOSTP, on the other hand, focuses on the transportation of solid materials, such as minerals or ores, and involves dealing with multiple competing objectives, such as cost, time, and quality of service. These problems are essential in logistics and supply chain management, where decision-makers must make optimal transportation plans by considering multiple objectives. Researchers and practitioners often employ optimization techniques, such as mathematical programming, heuristics, and meta-heuristics, to address these challenges efficiently [ 15 ].

Efficient transportation planning is essential for moving goods from their source to the destination. This process involves booking different types of vehicles and minimizing the total transportation time and cost is a crucial factor to consider. Various challenges can affect the optimal transportation policy, such as the weight and volume of products, the availability of specific vehicles, and other uncertain parameters. In this regard, several studies have proposed different approaches to solve the problem of multi-objective solid transportation under uncertainty. One such study by Kar et al. [ 16 ] used fuzzy parameters to account for uncertain transportation costs and time, and two methods were employed to solve the problem, namely the Zimmermann Method and the Global Criteria Method.

Similarly, Mirmohseni et al. [ 17 ] proposed a fuzzy interactive probabilistic programming approach, while Kakran et al. [ 18 ] addressed a multi-objective capacitated solid transportation problem with uncertain zigzag variables. Additionally, Chen et al. [ 19 ] investigated an uncertain bicriteria solid transportation problem by using uncertainty theory properties to transform the models into deterministic equivalents, proposing two models, namely the expected value goal programming and chance-constrained goal programming models [ 20 ]. These studies have contributed to developing different approaches using fuzzy programming, uncertainty theory, and related concepts to solve multi-objective solid transportation problems with uncertain parameters.

This paper presents a case study carried out on a private sector company in Egypt intending to ascertain the minimum number of trucks required to fulfill the decision-makers’ objectives of transporting the company’s fleet of trucks from multiple sources to various destinations. This objective is complicated by the diversity of truck types and transported products, as well as the decision-makers’ multiple priorities, specifically the cost of fuel consumption and the timeliness of truck arrival.

In contrast to previous research on the transportation problem, this paper introduces a novel approach that combines the multi-dimensional solid transportation problem framework with a multi-objective optimization technique. Building upon previous studies, which often focused on single-objective solutions and overlooked specific constraints, our research critically analyzes the limitations of these approaches. We identify the need for comprehensive solutions that account for the complexities of diverse truck fleets and transported products, as well as the decision-makers’ multiple priorities. By explicitly addressing these shortcomings, our primary goal is to determine the minimum number of trucks required to fulfill the decision-makers’ objectives, while simultaneously optimizing fuel consumption and transportation timeliness. Through this novel approach, we contribute significantly to the field by advancing the understanding of the transportation problem and providing potential applications in various domains. Our research not only offers practical solutions for real-world scenarios but also demonstrates the potential for improving transportation efficiency and cost-effectiveness in other industries or contexts. The following sections will present a comparative analysis of the proposed work, highlighting the advancements and novelty introduced by our approach.

Methods/experimental

This study uses a case study from Egypt to find the optimal distribution of a private sector company’s truck fleet under various optimization and multi-objective conditions. Specifically, the study aims to optimize the distribution of a private sector company’s truck fleet by solving a multi-objective solid transportation problem (MOSTP) and comparing three different methods for decision-making.

Design and setting

This study uses a case study design in a private sector company in Egypt. The study focuses on distributing the company’s truck fleet to transport products from factories to distribution centers.

Participants or materials

The participants in this study are the transportation planners and managers of the private sector company in Egypt. The materials used in this study include data on the truck fleet, sources, destinations, and products.

Processes and methodologies

The study employs the multi-objective multi-dimensional solid transportation problem (MOMDSTP) to determine the optimal solution for the company’s truck fleet distribution, considering two competing objectives: fuel consumption cost and total shipping time. The MOMDSTP considers the number and types of trucks, sources, destinations, and products and considers the supply and demand constraints.

To solve the MOMDSTP, three decision-making methods are employed: Zimmermann Programming Technique, Global Criteria Method, and Minimum Distance Method. The first two methods directly yield the best compromise solution (BCS), whereas the last method generates non-dominated extreme points by assigning different weights to each objective. Lingo software is used to obtain the optimal solutions for fuel consumption cost and time and the BCS and solutions with different weights for both objectives.

Ethics approval and consent

This study does not involve human participants, data, or tissue, nor does it involve animals. Therefore, ethics approval and consent are not applicable.

Statistical analysis

Statistical analysis is not conducted in this study. However, the MOMDSTP model and three well-established decision-making methods are employed to derive the optimal distribution of the company’s truck fleet under various optimization and multi-objective conditions.

In summary, this study uses a case study design to find the optimal distribution of a private sector company’s truck fleet under various optimization and multi-objective conditions. The study employs the MOMDSTP and three methods for decision-making, and data on the truck fleet, sources, destinations, and products are used as materials. Ethics approval and consent are not applicable, and statistical analysis is not performed.

Multi-objective transportation problem

The multi-objective optimization problem is a complex issue that demands diverse approaches to determine the most satisfactory solution. Prevalent techniques employed in this domain include the Weighted Sum Method, Minimum Distance Method, Zimmermann Programming Technique, and Global Criteria Method. Each method offers its own benefits and limitations, and the selection of a specific method depends on the nature of the problem and the preferences of the decision-makers [ 21 ].

This section discusses various methodologies employed to identify the most optimal solution(s) for the multi-objective multi-dimensional solid transportation problem (MOMDSTP), which is utilized as the basis for the case study. These methodologies encompass the Minimum Distance Method (MDM), the Zimmermann Programming Technique, and the Global Criteria Method [ 22 ].

Zimmermann Programming Technique

The Zimmermann Programming Technique (ZPT) is a multi-objective optimization approach that was developed by Professor Hans-Joachim Zimmermann in the late 1970s. This technique addresses complex problems with multiple competing objectives that cannot be optimized simultaneously. Additionally, it incorporates the concept of an “aspiration level,” representing the minimum acceptable level for each objective. The aspiration level ensures that the solution obtained is satisfactory for each objective. If the solution does not meet the aspiration level for any objective, the weights are adjusted, and the optimization process is iterated until a satisfactory solution is obtained.

A key advantage of ZPT is its ability to incorporate decision-makers’ preferences and judgments into the decision-making process. The weights assigned to each objective are based on the decision-maker’s preferences, and the aspiration levels reflect their judgments about what constitutes an acceptable level for each objective [ 23 ].

The Zimmermann Programming Technique empowers decision-makers to incorporate multiple objectives and achieve a balanced solution. By assigning weights to objectives, a trade-off can be made to find a compromise that meets various criteria. For example, this technique can optimize cost, delivery time, and customer satisfaction in supply chain management [ 24 ]. However, the interpretation of results may require careful consideration, and computational intensity can increase with larger-scale and complex problems.

 In order to obtain the solution, each objective is considered at a time to get the lower and upper bounds for that objective. Let for objective, and are the lower (min) and upper (max) bounds. The membership functions of the first and second objective functions can be generated based on the following formula [ 25 ]:

Next, the fuzzy linear programming problem is formulated using the max–min operator as follows:

Maximize min \({\mu }_{k}\left({F}_{k}\left(x\right)\right)\)   

Subject to \({g}_{i}\left(x\right) \left\{ \le ,= , \ge \right\}{b}_{i}\mathrm{ where }\;i = 1, 2, 3, ..., m.\)   

Moreover, x ≥ 0.

Global Criteria Method

The Global Criteria Method is a multi-objective optimization method that aims to identify the set of ideal solutions based on predetermined criteria. This method involves defining a set of decision rules that assess the feasibility and optimality of the solutions based on the objectives and constraints [ 26 ]. By applying decision rules, solutions that fail to meet the predetermined criteria are eliminated, and the remaining solutions are ranked [ 27 ].

The Global Criteria Method assesses overall system performance, aiding decision-makers in selecting solutions that excel in all objectives. However, it may face challenges when dealing with conflicting objectives [ 28 ]. Furthermore, it has the potential to overlook specific details, and the choice of aggregation function or criteria can impact the results by favoring specific solutions or objectives.

Let us consider the following ideal solutions:

f 1* represents the ideal solution for the first objective function,

f 2* represents the ideal solution for the second objective function, and

n 1* represents the ideal solution for the nth objective function.

Objective function formula:

Minimize the objective function F  =  \(\sum_{k=1}^{n}{(\frac{{f}_{k}\left({x}^{*}\right)-{f}_{k}(x)}{{f}_{k}({x}^{*})})}^{p}\)

Subject to the constraints: g i ( x ) \(\le\) 0, i  = 1, 2,.., m

The function fk( x ) can depend on variables x 1 , x 2 , …, x n .

Minimum Distance Method

The Minimum Distance Method (MDM) is a novel distance-based model that utilizes the goal programming weighted method. The model aims to minimize the distances between the ideal objectives and the feasible objective space, leading to an optimal compromise solution for the multi-objective linear programming problem (MOLPP) [ 29 ]. To solve MOLPP, the proposed model breaks it down into a series of single objective subproblems, with the objectives transformed into constraints. To further enhance the compromise solution, priorities can be defined using weights, and a criterion is provided to determine the best compromise solution. A significant advantage of this approach is its ability to obtain a compromise solution without any specific preference or for various preferences.

The Minimum Distance Method prioritizes solutions that closely resemble the ideal or utopian solution, assisting decision-makers in ranking and identifying high-performing solutions. It relies on a known and achievable ideal solution, and its sensitivity to the chosen reference point can influence results. However, it does not provide a comprehensive trade-off solution, focusing solely on proximity to the ideal point [ 30 ].

The mathematical formulation for MDM for MOLP is as follows:

The formulation for multi-objective linear programming (MOLP) based on the minimum distance method is referred to[ 31 ]. It is possible to derive the multi-objective transportation problem with two objective functions using this method and its corresponding formula.

Subject to the following constraints:

f * 1 , f * 2 : the obtained ideal objective values by solving single objective STP.

w 1 , w 2 : weights for objective1 and objective2 respectively.

f 1, f 2: the objective values for another efficient solution.

d : general deviational variable for all objectives.

\({{c}_{ij}^{1}, c}_{ij}^{2}\) : the unit cost for objectives 1 and 2 from source i to destination j .

\({{x}_{ij}^{1}, x}_{ij}^{2}\) : the amount to be shipped when optimizing for objectives 1 and 2 from source i to destination j .

Mathematical model for STP

The transportation problem (TP) involves finding the best method to ship a specific product from a defined set of sources to a designated set of destinations, while adhering to specific constraints. In this case, the objective function and constraint sets take into account three-dimensional characteristics instead of solely focusing on the source and destination [ 32 ]. Specifically, the TP considers various modes of transportation, such as ships, freight trains, cargo aircraft, and trucks, which can be used to represent the problem in three dimensions When considering a single mode of transportation, the TP transforms into a solid transportation problem (STP), which can be mathematically formulated as follows:

The mathematical form of the solid transportation problem is given by [ 33 ]:

Subject to:

Z = the objective function to be minimized

m = the number of sources in the STP

n = the number of destinations in the STP

p = the number of different modes of transportation in the STP

x ijk represents the quantity of product transported from source i to destination j using conveyance k

c ijk = the unit transportation cost for each mode of transportation in the STP

a i = the amount of products available at source i

b j = the demand for the product at destination j

e k = the maximum amount of product that can be transported using conveyance k

The determination of the size of the fleet for each type of truck that is dispatched daily from each factory to all destinations for the transportation of various products is expressed formally as follows:

z ik denotes the number of trucks of type k that are dispatched daily from factory i .

C k represents the capacity of truck k in terms of the number of pallets it can transport.

x ijk denotes a binary decision variable that is set to one if truck k is dispatched from factory i to destination j to transport product p , and zero otherwise. The summation is performed over all destinations j and all products p .

This case study focuses on an Egyptian manufacturing company that produces over 70,000 pallets of various water and carbonated products daily. The company has 25 main distribution centers and eight factories located in different industrial cities in Egypt. The company’s transportation fleet consists of hundreds of trucks with varying capacities that are used to transport products from factories to distribution centers. The trucks have been classified into three types (type A, type B, and type C) based on their capacities. The company produces three different types of products that are packaged in pallets. It was observed that the sizes and weights of the pallets are consistent across all product types The main objective of this case study is to determine the minimum number of each truck type required in the manufacturer’s garage to minimize fuel consumption costs and reduce product delivery time.

The problem was addressed by analyzing the benefits of diversifying trucks and implementing the solid transport method. Subsequently, the problem was resolved while considering the capacities of the sources and the requirements of the destinations. The scenario involved shipping products using a single type of truck, and the fuel consumption costs were calculated accordingly. The first objective was to reduce the cost of fuel consumption on the one-way journey from the factories to the distribution centers. The second objective was to reduce the time of arrival of the products to the destinations. The time was calculated based on the average speed of the trucks in the company’s records, which varies depending on the weight and size of the transported goods.

To address the multiple objectives and the uncertainty in supply and demand, an approach was adopted to determine the minimum number of trucks required at each factory. This approach involved determining the maximum number of trucks of each type that should be present in each factory under all previous conditions. The study emphasizes the significance of achieving a balance between reducing transportation costs and time while ensuring trucks are capable of accommodating quantities of any size, thus avoiding underutilization.

Figure  1 presents the mean daily output, measured in pallets, for each factory across three distinct product types. Additionally, Fig.  2 displays the average daily demand, measured in pallets, for the distribution centers of the same three product types.

No. of pallets in each source

No. of pallets in each destination

Results and discussion

As a result of the case study, the single objective problems of time and fuel consumption cost have been solved. The next step is to prepare a model for the multi-objective multi-dimensional solid transportation problem. Prior to commencing, it is necessary to determine the upper and lower bounds for each objective.

Assuming the first objective is fuel consumption cost and the second objective is time, we calculate the upper and lower bounds as follows:

The lower bound for the first objective, “cost,” is generated from the optimal solution for its single-objective model, denoted as Z 1 ( x 1 ), and equals 70,165.50 L.E.

The lower bound for the second objective, “time,” is generated from the optimal solution for its single-objective model, denoted as Z 2 ( x 2 ), and equals 87,280 min.

The upper bound for the first objective is obtained by multiplying c ijkp for the second objective by x ijkp for the first objective. The resulting value is denoted as Z 1 ( x 2 ) and equals 73,027.50 L.E.

The upper bound for the second objective is obtained by multiplying t ijkp for the first objective by x ijkp for the second objective. The resulting value is denoted as Z 2 ( x 1 ) and equals 88,286.50 min.

As such, the aspiration levels for each objective are defined from the above values by evaluating the maximum and minimum value of each objective.

The aspiration level for the first objective, denoted as F 1, ranges between 70,165.50 and 73,027.50, i.e., 70,165.50 <  =  F 1 <  = 730,27.50.

The aspiration level for the second objective, denoted as F 2, ranges between 87,280 and 88,286.50, i.e., 87,280 <  =  F 2 <  = 88,286.50.

The objective function for the multi-objective multidimensional solid transport problem was determined using the Zimmermann Programming Technique, Global Criteria Method, and Minimum Distance Method. The first two methods directly provided the best compromise solution (BCS), while the last method generated non-dominated extreme points by assigning different weights to each objective and finding the BCS from them. The best compromise solution was obtained using the Lingo software [ 34 ]. Table 1 and Fig.  3 present the objective values for the optimal solutions of fuel consumption cost and time, the best compromise solution, and solutions with different weights for both objectives. Figure  4 illustrates the minimum required number of each type of truck for daily transportation of various products from sources to destinations.

Objective value in different cases

Ideal distribution of the company’s truck fleet

The primary objective of the case study is to determine the minimum number of trucks of each type required daily at each garage for transporting products from factories to distribution centers. The minimum number of trucks needs to be flexible, allowing decision-makers to make various choices, such as minimizing fuel consumption cost, delivery time, or achieving the best compromise between different objectives. To determine the minimum number of required trucks, we compare all the previously studied cases and select the largest number that satisfies the condition: min Zik (should be set) = max Zik (from different cases). Due to the discrepancy between the truck capacity and the quantity of products to be transported, the required number of trucks may have decimal places. In such cases, the fraction is rounded to the nearest whole number. For example, if the quantity of items from a location requires one and a half trucks, two trucks of the specified type are transported on the first day, one and a half trucks are distributed, and half a truck remains in stock at the distribution center. On the next day, only one truck is transferred to the same distribution center, along with the semi-truck left over from the previous day, and so on. This solution may be preferable to transporting trucks that are not at full capacity. Table 2 and Fig.  5 depict the ideal distribution of the company’s truck fleet under various optimization and multi-objective conditions.

Min. No. of trucks should be set for different cases

Conclusions

In conclusion, this research paper addresses the critical issue of optimizing transportation within the context of logistics and supply chain management, specifically focusing on the methods known as the solid transportation problem (STP) and the multi-dimensional solid transportation problem (MDSTP). The study presents a case study conducted on a private sector company in Egypt to determine the optimal distribution of its truck fleet under different optimization and multi-objective conditions.

The research utilizes the multi-objective multi-dimensional solid transportation problem (MOMDSTP) to identify the best compromise solution, taking into account fuel consumption costs and total shipping time. Three decision-making methods, namely the Zimmermann Programming Technique, the Global Criteria Method, and the Minimum Distance Method, are employed to derive optimal solutions for the objectives.

The findings of this study make a significant contribution to the development of approaches for solving multi-objective solid transportation problems with uncertain parameters. The research addresses the complexities of diverse truck fleets and transported products by incorporating fuzzy programming, uncertainty theory, and related concepts. It critically examines the limitations of previous approaches that often focused solely on single-objective solutions and overlooked specific constraints.

The primary objective of this research is to determine the minimum number of trucks required to fulfill decision-makers objectives while optimizing fuel consumption and transportation timeliness. The proposed approach combines the framework of the multi-dimensional solid transportation problem with a multi-objective optimization technique, offering comprehensive solutions for decision-makers with multiple priorities.

This study provides practical solutions for real-world transportation scenarios and demonstrates the potential for enhancing transportation efficiency and cost-effectiveness in various industries or contexts. The comparative analysis of the proposed work highlights the advancements and novelty introduced by the approach, emphasizing its significant contributions to the field of transportation problem research.

Future research should explore additional dimensions of the multi-objective solid transportation problem and incorporate other decision-making methods or optimization techniques. Additionally, incorporating uncertainty analysis and sensitivity analysis can enhance the robustness and reliability of the proposed solutions. Investigating the applicability of the approach in diverse geographical contexts or industries would yield further insights and broaden the potential applications of the research findings.

Availability of data and materials

The data that support the findings of this study are available from the company but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request. Please note that some data has been mentioned in the form of charts as agreed with the company.

Abbreviations

Solid transportation problem

Multi-objective solid transportation problems

Multi-dimensional solid transportation problem

Multi-objective multi-dimensional solid transportation problem

Best compromise solution

Taha HA (2013) Operations research: an introduction. Pearson Education India

Li T et al (2020) Federated optimization in heterogeneous networks. Proc Mach Learn Syst 2:429–450

Google Scholar  

Babu MA et al (2020) A brief overview of the classical transportation problem.

Winston WL (2022) Operations research: applications and algorithms. Cengage Learning

Pratihar J et al (2020) Transportation problem in neutrosophic environment, in Neutrosophic graph theory and algorithms. IGI Global, p 180–212

Guo G, Obłój J (2019) Computational methods for martingale optimal transport problems. Ann Appl Probab 29(6):3311–3347

Article   MathSciNet   MATH   Google Scholar  

Marwan M (2022) Optimasi biaya distribusi material Dengan Metode NWC (North West Corner) DAN Metode VAM (Vogel Approximation Method) PADA PT XYZ. IESM J (Indust Eng Syst Manage J) 2(2):137–146

Qiuping N et al (2023) A parametric neutrosophic model for the solid transportation problem. Manag Decis 61(2):421–442

Article   Google Scholar  

Singh S, Tuli R, Sarode D (2017) A review on fuzzy and stochastic extensions of the multi index transportation problem. Yugoslav J Oper Res 27(1):3–29

Baidya A, Bera UK (2019) Solid transportation problem under fully fuzzy environment. Int J Math Oper Res 15(4):498–539

Berbatov K et al (2022) Diffusion in multi-dimensional solids using Forman’s combinatorial differential forms. Appl Math Model 110:172–192

Carlier G (2003) On a class of multidimensional optimal transportation problems. J Convex Anal 10(2):517–530

MathSciNet   MATH   Google Scholar  

Zaki SA et al (2012) Efficient multiobjective genetic algorithm for solving transportation, assignment, and transshipment problems. Appl Math 03(01):92–99

Article   MathSciNet   Google Scholar  

Latpate R, Kurade SS (2022) Multi-objective multi-index transportation model for crude oil using fuzzy NSGA-II. IEEE Trans Intell Transp Syst 23(2):1347–1356

Bélanger V, Ruiz A, Soriano P (2019) Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles. Eur J Oper Res 272(1):1–23

Kar MB et al (2018) A multi-objective multi-item solid transportation problem with vehicle cost, volume and weight capacity under fuzzy environment. J Intell Fuzzy Syst 35(2):1991–1999

Mirmohseni SM, Nasseri SH, Zabihi A (2017) An interactive possibilistic programming for fuzzy multi objective solid transportation problem. Appl Math Sci 11:2209–2217

Kakran VY, Dhodiya JM (2021) Multi-objective capacitated solid transportation problem with uncertain variables. Int J Math, Eng Manage Sci 6(5):1406–1422

MATH   Google Scholar  

Chen L, Peng J, Zhang B (2017) Uncertain goal programming models for bicriteria solid transportation problem. Appl Soft Comput 51:49–59

Khalifa HAE-W, Kumar P, Alharbi MG (2021) On characterizing solution for multi-objective fractional two-stage solid transportation problem under fuzzy environment. J Intell Syst 30(1):620–635

El-Shorbagy MA et al (2020) Evolutionary algorithm for multi-objective multi-index transportation problem under fuzziness. J Appl Res Ind Eng 7(1):36–56

Uddin MS et al (2021) Goal programming tactic for uncertain multi-objective transportation problem using fuzzy linear membership function. Alex Eng J 60(2):2525–2533

Hosseinzadeh E (2023) A solution procedure to solve multi-objective linear fractional programming problem in neutrosophic fuzzy environment . J Mahani Math Res. 111–126.  https://jmmrc.uk.ac.ir/article_3728_bc0be59dc0f595cc32faae1991cd12f9.pdf

Jagtap K, and Kawale S (2017) Multi-Dimensional-Multi-Objective-Transportation-Problem-by-Goal-Programming . Int J Sci Eng Res 8(6):568–573

Paratne P, and Bit A (2019) Fuzzy programming technique with new exponential membership function for the solution of multiobjective transportation problem with mixed constraints. J Emerg Technol Innov Res.  https://www.researchgate.net/profile/Mohammed-Rabie-3/publication/363480949_A_case_study_on_the_optimization_of_multi-objective_functions_transportation_model_for_public_transport_authority_Egypt/links/631f0549071ea12e362a9214/A-case-study-on-the-optimization-of-multi-objective-functions-transportation-model-for-public-transport-authority-Egypt.pdf

Annamalaınatarajan R, and Swaminathan M (2021) Uncertain multi–objective multi–item four dimensional fractional transportation model . Ann Rom Soc Cell Biol. 231–247.  https://www.annalsofrscb.ro/index.php/journal/article/download/2457/2063

Mohammed A (2020) Towards a sustainable assessment of suppliers: an integrated fuzzy TOPSIS-possibilistic multi-objective approach. Ann Oper Res 293:639–668

Umarusman N (2019) Using global criterion method to define priorities in Lexicographic goal programming and an application for optimal system design . MANAS Sosyal Araştırmalar Dergisi. 8(1):326–341

Kamal M et al (2018) A distance based method for solving multi-objective optimization problems . J Mod Appl Stat Methods 17(1).  https://digitalcommons.wayne.edu/jmasm/vol17/iss1/21

Kaur L, Rakshit M, Singh S (2018) A new approach to solve multi-objective transportation problem. Appl Appl Math: Int J (AAM) 13(1):10

Kamal M et al (2018) A distance based method for solving multi-objective optimization problems. J Mod Appl Stat Methods 17(1):21

Yang L, Feng Y (2007) A bicriteria solid transportation problem with fixed charge under stochastic environment. Appl Math Model 31(12):2668–2683

Article   MATH   Google Scholar  

Munot DA, Ghadle KP (2022) A GM method for solving solid transportation problem. J Algebraic Stat 13(3):4841–4846

Gupta N, and Ali I (2021) Optimization with LINGO-18 problems and applications. CRC Press

Download references

Acknowledgements

Not applicable.

Author information

Authors and affiliations.

Automotive and Tractor Engineering Department, Minia University, Minia, Egypt

Mohamed H. Abdelati, Ali M. Abd-El-Tawwab & M Rabie

Automotive and Tractor Engineering Department, Helwan University, Mataria, Egypt

Elsayed Elsayed M. Ellimony

You can also search for this author in PubMed   Google Scholar

Contributions

MHA designed the research study, conducted data collection, analyzed the data, contributed to the writing of the paper, and reviewed and edited the final manuscript. AMA contributed to the design of the research study, conducted a literature review, analyzed the data, contributed to the writing of the paper, and reviewed and edited the final manuscript. EEME contributed to the design of the research study, conducted data collection, analyzed the data, contributed to the writing of the paper, and reviewed and edited the final manuscript.MR contributed to the design of the research study, conducted programming using Lingo software and others, analyzed the data, contributed to the writing of the paper, and reviewed and edited the final manuscript. All authors have read and approved the manuscript.

Corresponding author

Correspondence to Mohamed H. Abdelati .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Abdelati, M.H., Abd-El-Tawwab, A.M., Ellimony, E.E.M. et al. Solving a multi-objective solid transportation problem: a comparative study of alternative methods for decision-making. J. Eng. Appl. Sci. 70 , 82 (2023). https://doi.org/10.1186/s44147-023-00247-z

Download citation

Received : 19 April 2023

Accepted : 27 June 2023

Published : 20 July 2023

DOI : https://doi.org/10.1186/s44147-023-00247-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Decision-making
• Multi-objective
• Solid transpiration

Applications of Transportation Models in Africa

• First Online: 10 November 2022

Cite this chapter

• Houda Alaya 2  

Part of the book series: Contributions to Management Science ((MANAGEMENT SC.))

155 Accesses

Ample infrastructure, such as roads and proper vehicles to get goods to the right place at the right time, is an all-important factor in defining the quality of a transportation system. This has always been and continues to be a major challenge for developing countries such as most African countries. For decades, in Africa, urban development and transportation infrastructures have been inextricably interwoven. Cities began to grow, resulting in greater distances between activity locations and people’s homes. The number of cars on the road and heavy traffic have increased even more, and transportation development projects are planned in different regions of Africa. Several papers have investigated the impact of African infrastructure on the transportation of materials such as oil and food and attempted to solve these problems by proposing mathematical models or algorithms that aimed to optimize one or more objectives, such as total travel cost or travel time.

In this chapter, we survey African transportation case studies and provide a critical perspective on the use of transportation models. We review real-life case studies in Africa related to transportation improvement and waste management. For each situation, we stress the recommended model or algorithm and then assess the outcomes of the proposed models and their impact on the African transportation progress.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Urban Transportation Scenarios in a LUCC Model: A Case Study in Bogota, Colombia

The Value of Freight Accessibility: a Spatial Analysis in the Tampa Bay Region

A Simulation Platform for Transportation, Land Use and Mobile Source Emissions

Badran, M. F., & El-Haggar, S. M. (2006). Optimization of municipal solid waste management in Port Said–Egypt. Waste Management, 26 (5), 534–545.

Article   Google Scholar  

Ben Abdelaziz, F., Masri, H., & Alaya, H. (2017). A recourse goal programming approach for airport bus routing problem. Annals of Operations Research, 251 (1), 383–396.

Bereketli, I., Genevois, M. E., Albayrak, Y. E., & Ozyol, M. (2011). WEEE treatment strategies’ evaluation using fuzzy LINMAP method. Expert Systems with Applications, 38 (1), 71–79.

Beukes, E. A., Vanderschuren, M. J. W. A., & Zuidgeest, M. H. P. (2011). Context sensitive multimodal road planning: A case study in Cape Town, South Africa. Journal of Transport Geography, 19 (3), 452–460.

Bimir, M. N. (2020). Revisiting e-waste management practices in selected African countries. Journal of the Air & Waste Management Association, 70 (7), 659–669.

Cao, W., Çelik, M., Ergun, Ö., Swann, J., & Viljoen, N. (2016). Challenges in service network expansion: An application in donated breastmilk banking in South Africa. Socio-Economic Planning Sciences, 53 , 33–48.

Catalani, M., & Zamparelli, S. (2017). Maritime transport of oil and routing management. Journal of Traffic and Transportation Engineering, 5 , 117–123.

Google Scholar  

Chikodzi, D., Dube, K., & Ngcobo, N. (2021). Rethinking harbours, beaches and urban estuaries waste management under climate-induced floods in South Africa. In The Increasing Risk of Floods and Tornadoes in Southern Africa (pp. 127–140). Springer, .

Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, 6 (1), 80–91.

De Meyer, A., Cattrysse, D., & Van Orshoven, J. A. (2015). Generic mathematical model to optimise strategic and tactical decisions in biomass-based supply chains (OPTIMASS). European Journal of Operational Research, 245 (1), 247–264.

Euchi, J., & Mraihi, R. (2012). The urban bus routing problem in the Tunisian case by the hybrid artificial ant colony algorithm. Swarm and Evolutionary Computation, 2 , 15–24.

FC, O., Ogola, J. S., & Tshitangano, T. G. (2018). A review of medical waste management in South Africa. Open Environmental Sciences, 10 (1).

Gebremedhin, K. G., Gebremedhin, F. G., Amin, M. M., and Godfrey, L. K. (2018). State of waste management in Africa. United Nations Environment Programme.

Godfrey, L., Ahmed, M. T., Gebremedhin, K. G., Katima, J. H., Oelofse, S., Osibanjo, O., & Yonli, A. H. (2019). Solid waste management in Africa: Governance failure or development opportunity. Regional Development in Africa, 235 .

Groves, G., Le Roux, J., & Van Vuuren, J. H. (2004). Network service scheduling and routing. International Transactions in Operational Research, 11 (6), 613–643.

Hart, J. (2020). Histories of transportation and mobility in Africa. In Oxford research encyclopedia of African history .

Jammeli, H., Argoubi, M., & Masri, H. (2021). A Bi-objective stochastic programming model for the household waste collection and transportation problem: Case of the city of Sousse. Operational Research, 21 (3), 1613–1639.

Masache, A., Mashira, H., & Maposa, D. (2012). Antiretroviral drug distribution routing system in limpopo province of South Africa. Journal of Mathematics and System Science, 2 (8), 512.

Masri, H., Ben Abdelaziz, F., & Alaya, H. (2016). A recourse stochastic goal programming approach for the multi-objective stochastic vehicle routing problem. Journal of Multi-Criteria Decision Analysis, 23 (1–2), 3–14.

Montoya-Torres, J. R., Franco, J. L., Isaza, S. N., Jiménez, H. F., & Herazo-Padilla, N. (2015). A literature review on the vehicle routing problem with multiple depots. Computers & Industrial Engineering, 79 , 115–129.

Muttiah, R. S., Engel, B. A., & Jones, D. D. (1996). Waste disposal site selection using GIS-based simulated annealing. Computers and Geosciences, 22 (9), 1013–1017.

Nemathaga, F., Maringa, S., & Chimuka, L. (2008). Hospital solid waste management practices in Limpopo Province, South Africa: A case study of two hospitals. Waste Management, 28 (7), 1236–1245.

Njoh, A. J. (2000). Transportation infrastructure and economic development in sub-Saharan Africa. Public Works Manage. Policy, 4 (4), 286–296.

Njoh, A. J. (2008). Implications of Africa’s transportation systems for development in the era of globalization. Review of Black Political Economy, 35 (4), 147–162.

Njoh, A. J. (2009). The development theory of transportation infrastructure examined in the context of central and West Africa. Review of Black Political Economy, 36 (3–4), 227–243.

Njoh, A. J. (2012). Impact of transportation infrastructure on development in East Africa and the Indian Ocean Region. Journal of Urban Planning and Development, 138 (1), 1–9.

Nkosi, N., Muzenda, E., Zvimba, J., and Pilusa, J. (2013). The current waste generation and management trends in South Africa: A review .

Onibokun, A. G., & Kumuyi, A. J. (1999). Governance and waste management in Africa. In Managing the monster: Urban waste and governance in Africa . IDRC, .

Ozment, J. (2006). Assessing transportation contributions to the economic performance of developing countries . Final project report, college of business administration, University of Arkansas.

Semba, S., & Mujuni, E. (2019). An empirical performance comparison of meta-heuristic algorithms for school bus routing problem. Tanzania Journal of Science, 45 (1), 81–92.

Tavares, G., Zsigraiova, Z., Semiao, V., & Carvalho, M. D. G. (2009). Optimisation of MSW collection routes for minimum fuel consumption using 3D GIS modeling. Waste Management, 29 (3), 1176–1185.

Tavares, G., Zsigraiova, Z., Semiao, V., & da Graça Carvalho, M. (2008). A case study of fuel savings through optimisation of MSW transportation routes. Management of Environmental Quality: An International Journal .

Vadenbo, C., Guillén-Gosálbez, G., Saner, D., & Hellweg, S. (2014). Multi-objective optimization of waste and resource management in industrial networks–Part II: Model application to the treatment of sewage sludge. Resources, Conservation and Recycling, 89 , 41–51.

Willemse, E. J., & Joubert, J. W. (2012). Applying min–max k postmen problems to the routing of security guards. Journal of the Operational Research Society, 63 (2), 245–260.

Xin, X., Wang, X., Ma, L., Chen, K., & Ye, M. (2021). Shipping network design–infrastructure investment joint optimization model: A case study of West Africa. Maritime Policy & Management, 1-27 .

Zhu, C., & Zhu, X. (2022). Multi-objective path-decision model of multimodal transport considering uncertain conditions and carbon emission policies. Symmetry, 14 (2), 221.

Download references

Acknowledgments

We thank Donald Simpson of the Obex project for providing editorial support.

Author information

Authors and affiliations.

Business Analytics and Decision Making Laboratory, Tunis Business School, Tunis, Tunisia

Houda Alaya

You can also search for this author in PubMed   Google Scholar

Editor information

Editors and affiliations.

College of Business Administration, University of Bahrain, Sakhir, Bahrain

Hatem Masri

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Alaya, H. (2022). Applications of Transportation Models in Africa. In: Masri, H. (eds) Africa Case Studies in Operations Research. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-031-17008-9_7

Download citation

DOI : https://doi.org/10.1007/978-3-031-17008-9_7

Published : 10 November 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-17007-2

Online ISBN : 978-3-031-17008-9

eBook Packages : Mathematics and Statistics Mathematics and Statistics (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Computer Science and Engineering
• Computing in Mathematics
• Optimization (Mathematical Programming)
• Computing in Mathematics, Natural Science, Engineering and Medicine
• Linear Programming

Application of Linear Programming on a Transportation Problem

• Conference: Innovation Management and information Technology impact on Global Economy in the Era of Pandemic
• At: Cordoba, Spain

• University of Eastern Finland
• This person is not on ResearchGate, or hasn't claimed this research yet.

• Covenant University Ota Ogun State, Nigeria

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Akehwe O. E.

• Adekunle Ezekiel Adeyosoye

• J W Chinneck
• M A Schulze
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Paper Information

• Paper Submission

Journal Information

• About This Journal
• Editorial Board
• Current Issue
• Author Guidelines

International Journal of Traffic and Transportation Engineering

p-ISSN: 2325-0062    e-ISSN: 2325-0070

2020;  9(2): 37-40

doi:10.5923/j.ijtte.20200902.02

An Optimal Solution for a Real Transportation Problem with Lingo Code

Hilal A. Abdelwali , Mohsen Alardhi , Ahmad E. Murad, Jasem M.S. Al-Rajhi

Automotive and Marine Department, College of Technological Studies, PAAET, Kuwait

Copyright © 2020 The Author(s). Published by Scientific & Academic Publishing.

The transportation problem deals with finding the minimum cost of transporting a single product from a certain number of sources to a given number of destinations. The problem can be expressed by the formulation of a linear model, and it can be solved using the simplex algorithm. But due to its particular structure of the linear model, it can be solved with more efficient methods. LINGO is one of the compelling tools which can be used to solve optimization problems. It includes integrated packages that include powerful language for expressing related optimization models, a full-featured environment for both building and editing problems, and a set of fast built-in solvers. In this paper, a lingo code is prepared based on the formulation of transportation problems to solve an actual case study. The optimal solution of the studied case study reduces the total ton-kilometers by 9.1702 % of the real distribution amounts. The real data, the lingo code, the optimal solution, and a comparison between the real and optimal solutions are both included.

Keywords: Transportation problem, an actual case study, LINGO

Cite this paper: Hilal A. Abdelwali, Mohsen Alardhi, Ahmad E. Murad, Jasem M.S. Al-Rajhi, An Optimal Solution for a Real Transportation Problem with Lingo Code, International Journal of Traffic and Transportation Engineering , Vol. 9 No. 2, 2020, pp. 37-40. doi: 10.5923/j.ijtte.20200902.02.

Article Outline

1. introduction, 2. transportation problem, 4. case study.

The optimal versus actual distribution of M.E.M.C.       5. Conclusions [1]   Singh S. Note on transportation problem with new method for resolution of degeneracy. Universal Journal of Industrial and Business Management. 2015; 3: 26-36. [2]   El-Sayed M. Ellaimony, Hilal A. Abdelwali, Jasem M. Al-Rajhi, Mohsen S. Al-Ardhi, Yousef Alhouli. Solution of a class of bi-criteria multistage transportation problem using dynamic programming technique. International Journal of Traffic and Transportation Engineering. 2015; 4: 115-22. [3]   Hillier FS. Introduction to Operations Research with Access Card for Premium Content: McGraw-Hill Education; 2014. [4]   Taha HA. Operations Research: An Introduction: Prentice Hall; 2011. [5]   Lee SM, Olson DL. Introduction to Management Science: Thomson; 2005. [6]   Ellaimony E. Different Algorithms for Solving Multi-Stage Transportation Problems. Egypt: Helwan University; 1985. [7]   Alrajhi J, Hilal A. Abdelwali, Elsayed E.M. Ellaimony and Swilem A.M. Swilem. A Decomposition Algorithm for Solving A Class of Bi-Criteria Multistage Transportation Problem With Case Study. International Journal of Innovative Research in Science Engineering and Technology. September, 2013; 2. [8]   Alkhulaifi JA, Hilal A. Abdelwali, Mohsen AlArdhi, Elsayed E. M. Ellaimony, Khalid. An Algorithm for Solving Bi-criteria Large Scale Transshipment Problems. The Global Journal of Researches in Engineering. 2014; Vol 14. [9]   El-Wahed WFA, Lee SM. Interactive fuzzy goal programming for multi-objective transportation problems. Omega. 2006; 34: 158-66. [10]   Ringuest JL, Rinks DB. Interactive solutions for the linear multiobjective transportation problem. European Journal of Operational Research. 1987; 32: 96-106. [11]   Abdelwali HA, Swilem SM, Shiaty RE, Murad MM. Solving A Transportation Problem Actual Problem Using Excel Solver. International Journal of Engineering and Technical Research. December 2019; 9. [12]   Abdelati MH. Vehicle Transportation Policy Using an Interactive Fuzzy Multi-objective Technique. Minia, Egypt: Minia University; October 2019. [13]   Schrage LE. Optimization Modeling with LINDO. 5, revised ed: Duxbury Press; 1997. [14]   Anand Jayakumar A RP, . Solving a Simple Transportation Problem Using LINGO. International Journal of Innovative Science, Engineering & Technology. April 2018; 5. [15]   AP AJ, Raghunayagan P. Solving a Multistage Transportation Problem Using LINGO. International Journal of Innovative Science, Engineering & Technology. April 2018; 5. [16]   Balaji N, A.N. Balaji. Solving the multi-period fixed cost transportation problem using LINGO solver. International Journal of Pure and Applied Mathematics. 2018; 119: 2151-7. Share full article For more audio journalism and storytelling, download New York Times Audio , a new iOS app available for news subscribers. Apple Podcasts Google Podcasts An Escalating War in the Middle East Tensions are on a knife edge after israel carried out a strike on the hezbollah leader allegedly behind an attack in the golan heights.. Hosted by Sabrina Tavernise Featuring Ben Hubbard Produced by Rachelle Bonja and Sydney Harper With Shannon M. Lin and Will Reid Edited by Lexie Diao and Patricia Willens Original music by Dan Powell and Sophia Lanman Engineered by Chris Wood Listen and follow The Daily Apple Podcasts | Spotify | Amazon Music | YouTube Warning: This episode contains audio of war. Over the past few days, the simmering feud between Israel and the Lebanese militia Hezbollah, has reached a critical moment. Ben Hubbard, the Istanbul bureau chief for The New York Times, explains why the latest tit-for-tat attacks are different and why getting them to stop could be so tough. On today’s episode Ben Hubbard , the Istanbul bureau chief for The New York Times. Background reading Israel says it killed a Hezbollah commander , Fuad Shukr, in an airstrike near Beirut. The Israeli military blamed Mr. Shukr for an assault on Saturday that killed 12 children and teenagers in the Israeli-controlled Golan Heights. There are a lot of ways to listen to The Daily. Here’s how. We aim to make transcripts available the next workday after an episode’s publication. You can find them at the top of the page. The Daily is made by Rachel Quester, Lynsea Garrison, Clare Toeniskoetter, Paige Cowett, Michael Simon Johnson, Brad Fisher, Chris Wood, Jessica Cheung, Stella Tan, Alexandra Leigh Young, Lisa Chow, Eric Krupke, Marc Georges, Luke Vander Ploeg, M.J. Davis Lin, Dan Powell, Sydney Harper, Michael Benoist, Liz O. Baylen, Asthaa Chaturvedi, Rachelle Bonja, Diana Nguyen, Marion Lozano, Corey Schreppel, Rob Szypko, Elisheba Ittoop, Mooj Zadie, Patricia Willens, Rowan Niemisto, Jody Becker, Rikki Novetsky, Nina Feldman, Will Reid, Carlos Prieto, Ben Calhoun, Susan Lee, Lexie Diao, Mary Wilson, Alex Stern, Sophia Lanman, Shannon Lin, Diane Wong, Devon Taylor, Alyssa Moxley, Olivia Natt, Daniel Ramirez and Brendan Klinkenberg. Our theme music is by Jim Brunberg and Ben Landsverk of Wonderly. Special thanks to Sam Dolnick, Paula Szuchman, Lisa Tobin, Larissa Anderson, Julia Simon, Sofia Milan, Mahima Chablani, Elizabeth Davis-Moorer, Jeffrey Miranda, Maddy Masiello, Isabella Anderson, Nina Lassam and Nick Pitman. Ben Hubbard is the Istanbul bureau chief, covering Turkey and the surrounding region. More about Ben Hubbard Advertisement 484 IMAGES 8 A Case Study with Many Transportation Problems Applications of transportation problems in real life by swini chadha on TRANSPORTATION PROBLEM INTRODUCTION [Operations Research] Case Study: Transportation define optimal solution in transportation problem Case Study On Transportation Problem In The Philippines VIDEO Transportation Problem CASE STUDY TRANSPORTATION ISSUES AND CHALLENGES (AKRAM) The challenges & future of passenger rail transport in S.Africa CASE STUDY TRANSPORTATION APC Exam Case study on Khanna rail @ccident 1998 COMMENTS Transport Case Studies Policy aim. 1. Using existing powers over bus stops, timetables and routes. 2. Integrating the planning of bus services with other modes of transport in the region. 3. Giving cities the power to directly regulate bus services. 4. Providing long-term funding certainty for infrastructure. (PDF) Total Cost Minimization Transportation Problem -A Case Study of Vol ume-9 Issue-11 September 2020, DOI: 10.26671/IJIRG.2020.11.9.102 Page 97. Total Cost Minimization Transportation Problem - A Case Study of Carl Star. 1 Rakesh Agarwal, 2 Piyusha Somvanshi. 1 ... A CASE STUDY ON APPLICATION OF TRANSPORTATION PROBLEM case study "A Case Study on Application of Transportation Problem of some Select. Companies" conducted by Ahmed and Kalita. In this study, north-west corner rule, least. entry method, Vogel ... PDF Solving Real-Life Problem Using Transportation Step 3. In that minimum value, find the greatest maximum value thereby allocating the minimum capacity or subject demand to inferred cell. 4. If there is a tie in maximum value find the sum of row and column for the maximum value, in that which is maximum is given allocation. If in the maximum value, if the demand and supply are same, then ... A Comprehensive Literature Review on Transportation Problems So, to tackle all real-life situations on transportation problems, the decision-maker (DM) often needs to consider multiple non-commensurable or conflicting objectives in transportation problems. ... Ahmed, A.: Multi-choice multi-objective capacitated transportation problem: a case study of uncertain demand and supply. J. Stat. Manag. Syst. 21 ... (PDF) TRANSPORTATION PROBLEMS: APPLICATIONS Nowadays, the transaction of goods assumes a great importance on the daily life of both individual and collective entities. Their transportation within Europe relies mainly on road transport. Transportation: Articles, Research, & Case Studies on Transportation by George Serafeim and Katie Trinh. A systematic methodology for measuring product impact can be applied across a range of industries. Examining two competitor companies in the airlines industry, this study finds that analyzing each dimension of product impact allows for deeper understanding of each company's business strategies. 02 Feb 2016. The Problem of Sustainable Intermodal Transportation: A Case Study of Baykasoğlu, A.; Subulan, K. A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application. Trans. Res. Part E 2016, 95, 207-247. [Google Scholar] Tian, W.L.; Cao, C.X. A generalized interval fuzzy mixed integer programming model for a multimodal transportation problem under uncertainty. Insightful Transportation Case Studies to Read in 2022 One of the largest logistics platforms in the world, C.H. Robinsons is responsible with over $21 billion in freight, as well as 19 million shipments each year. The company helps keep crucial transportation elements moving in the modern world, but also solves complex logistics problems. During 2020 and 2021, the brand was forced to implement a ... PDF A case study of reducing the total wasted time for the bus movement of real-life, it is very difficult to collect accurate data due to the nature of production rates, and the changes in sources availabilities and destinations requirements, and many other reasons [5]. Dealing with inaccurate data is called fuzzy studies, and the transportation problem with fuzzy Analyzing multimodal transportation problem and its application to The basic transportation model was first initiated by Kantorovich [], who has prescribed an incomplete algorithm for calculating the solution of the transportation problem.Hitchcock [] studied the problem of minimizing cost of distribution of product from several warehouses to a number of purchasers.To accommodate the complexities in the real-world problems, the study on transportation problem ... A Game-Theoretic Approach to the Freight Transportation Pricing Problem It helps us formulate the transportation pricing problem as a mixed-integer linear model. A real-life case study is conducted to investigate the validity of the proposed models. In addition, the sustainability objectives of the government, as the strategic decision maker that determines fuel taxes, are examined. Solving a multi-objective solid transportation problem: a comparative The transportation problem in operations research aims to minimize costs by optimizing the allocation of goods from multiple sources to destinations, considering supply, demand, and transportation constraints. This paper applies the multi-dimensional solid transportation problem approach to a private sector company in Egypt, aiming to determine the ideal allocation of their truck fleet. PDF Multiobjective Transportation Problem Transportation of Fresh ... TRANSPORTATION OF FRESH WATER FISHES: A CASE STUDY USING MULTI- OBJECTIVE TRANSPORTATION PROBLEM. J. Irine1*, R. Sophia Porchelvi 2, R. Regupathi3 and S. Balasubramanian4. 1*Research Scholar, 2Associate Professor of Mathematics A.D.M College for Women (Autonomous), Nagapattinam, Tamil Nadu, India. 3Assistant Professor of Basic Engineering , 4 ... Applications of transportation problems in real life APPLICATIONS- Minimize shipping cost determine low cost location find minimum cost production schedule military distribution system Applications of transportation problems in real life. to find out locations of transportationcorporations depots where insignificant total cost A study of uncertain 4D transportation problems with rough interval To show the effectiveness and applicability of the proposed work in a real-world context, a real-life case study of the apple fruit transportation is solved. 4. Preliminaries. The definitions related to the rough set theory including the rough space, the rough set, the rough interval and the trust measure [36], [50], [51] are given in this ... Transportation problem, an actual case study, LINGO In this paper, a lingo code is prepared based on the formulation of transportation problems to solve an actual case study. The optimal solution of the studied case study reduces the total ton-kilometers by 9.1702 % of the real distribution amounts. The real data, the lingo code, the optimal solution, and a comparison between the real and ... Improving road transport operations through lean thinking: a case study Traditionally, logistics and transportation problems have been addressed through mathematical modelling, operations research, and simulation, but criticism has emerged about their effectiveness to actually address real-life problems. This paper documents a case study whereby the road transport operations of a leading Mexican brewery were ... Applications of Transportation Models in Africa In this chapter, we survey African transportation case studies and provide a critical perspective on the use of transportation models. We review real-life case studies in Africa related to transportation improvement and waste management. ... Table 3 details solved real-life routing problems in different African countries. Table 3 An overview of ... PDF Real Life Application of Transportation Problem in Malaysian ... The study highlighted the applications of VAM and MODI in form of linear programming and spreadsheet in a case study of a transportation problem of a Malaysian Petrochemical Company. Optimum plan and solution to minimize the total cost of transportation were formulated and analysed. Application of Linear Programming on a Transportation Problem Transportation problem (TP) is an important network structured linear programming problem that arises in several contexts and has deservedly received a great deal of attention in the literature. The Sunday Read: 'The Kidnapping I Can't Escape' The Surprise Ending to the Mar-a-Lago Documents Case The Sunday Read: 'The Kidnapping I Can't Escape' Fifty years ago, her father's friend was taken at gunpoint on Long Island. An Optimal Solution for a Real Transportation Problem with Lingo Code Lingo code for the studied transportation problem. To compare the results of the LINGO code, i.e. the optimal solution with the actual distribution of the problem, we add the actual distribution of the studied transportation problem case study in Table (2). Total ton-kilometers of the actual distribution is 973,060. Table 2. An Escalating War in the Middle East Warning: This episode contains audio of war. Over the past few days, the simmering feud between Israel and the Lebanese militia Hezbollah, has reached a critical moment. PDF International Journal of Technology, Management and Social Sciences case study "A Case Study on Application of Transportation Problem of some Select Companies" conducted by Ahmed and Kalita. In this study, north-west corner rule, least entry method, Vogel's Approximation methods are applied to get the minimum cost. Optimality is tested and compared the results that are solved by Simplex method using assignment essay homework paper phd report resume review speech thesis