assignment model application

The is a special form of a linear programming model that is similar to the transportation model. There are differences, however. In the assignment model, the supply at each source and the demand at each destination are each limited to one unit.

The following example will demonstrate the assignment model. The Atlantic Coast Conference (ACC) has four basketball games on a particular night. The conference office wants to assign four teams of officials to the four games in a way that will minimize the total distance traveled by the officials. The supply is always one team of officials, and the demand is for only one team of officials at each game. The distances in miles for each team of officials to each game location are shown in the following table:

The travel distances to each game for each team of officials

The linear programming formulation of the assignment model is similar to the formulation of the transportation model, except all the supply values for each source equal one, and all the demand values at each destination equal one. Thus, our example is formulated as follows :

This is a balanced assignment model. An unbalanced model exists when supply exceeds demand or demand exceeds supply.

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Application of assignment model in resources utilization of University Service Centre

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Teoh Siew Chin , Mohammed Syazwan Mohd Shokri , Nerda Zura Zaibidi; Application of assignment model in resources utilization of University Service Centre. AIP Conf. Proc. 21 August 2019; 2138 (1): 040008. https://doi.org/10.1063/1.5121087

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University Service Centre (USC) is a one-stop centre for students to acquire all services regarding their studies at a university. It is observed that the waiting time for students to get the services is quite long for some counters. This scenario will give negative feedback and bad reputation to the USC performance. Thus, the purpose of this study is to develop assignment models of counters to services for staff utilization in order to minimize total service time by using Hungarian method. The waiting time of students being served during one-month period is collected and the record shows that there were 7043 students have attended to the services within 17 operation days. The assignment models have been developed to minimize the total service time by considering few scenarios of assigning services to counters. The results show that the total service time can be reduced by having several alternative scenarios. This research has suggested the minimum time of services that can be a guideline for policy making in order to improve the service performance and students’ satisfaction.

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Basic Static Assignment to Transportation Networks

• First Online: 01 January 2009

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• Ennio Cascetta 2  

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 29))

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Traffic assignment models simulate the interaction of demand and supply on a transportation network. These models allow calculation of performance measures and user flows for each supply element (network link), resulting from origin–destination (O-D) demand flows, path choice behavior, and the mutual interactions between supply and demand.

Giulio Erberto Cantarella is co-author of this chapter.

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Dipartimento di Ingegneria dei Trasporti, Università degli Studi di Napoli Federico II, Via Claudio, 21, 80125, Napoli, Italy

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Cascetta, E. (2009). Basic Static Assignment to Transportation Networks. In: Transportation Systems Analysis. Springer Optimization and Its Applications, vol 29. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-75857-2_5

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Quantitative Techniques: Theory and Problems by P. C. Tulsian, Vishal Pandey

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WHAT IS ASSIGNMENT PROBLEM

Assignment Problem is a special type of linear programming problem where the objective is to minimise the cost or time of completing a number of jobs by a number of persons.

The assignment problem in the general form can be stated as follows:

“Given n facilities, n jobs and the effectiveness of each facility for each job, the problem is to assign each facility to one and only one job in such a way that the measure of effectiveness is optimised (Maximised or Minimised).”

Several problems of management has a structure identical with the assignment problem.

Example I A manager has four persons (i.e. facilities) available for four separate jobs (i.e. jobs) and the cost of assigning (i.e. effectiveness) each job to each ...

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Scottish VAT Assignment Experimental Statistics 2022

This release of experimental statistics on Scottish VAT assignment will include the 2022 estimate and further historical estimates in the back series.  The VAT assignment model calculates the Scottish share of UK VAT for the purposes of Scottish VAT assignment.  This release will be published at 9:30am on 26 September 2024.  If you have any further queries, please contact:  [email protected]

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Unpacking the albeast flaw.

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ALBeast, a flaw within AWS Application Load Balancer authentication discovered by Miggo, illustrates ... [+] evolving challenges with the shared responsibility model.

In the rapidly evolving landscape of cloud computing, security remains a top concern—one that continues to challenge both providers and users. The recent discovery of the ALBeast vulnerabilit y, a critical flaw in AWS’s Application Load Balancer (ALB) authentication, serves as a stark reminder that even the most trusted cloud services are not immune to serious security risks.

Jason Soroko , senior vice president of product at Sectigo, explained that the ALBeast issue arises not from a flaw in the ALB itself but from how it’s configured by users. “The issue involves improper authentication setups, where apps fail to validate the token signer or mistakenly accept traffic from sources other than their ALB. This can allow unauthorized access to resources and data exfiltration.”

The ALBeast flaw is not just a technical issue, but a crucial test of the shared responsibility model that underpins cloud security.

The ALBeast Vulnerability: More Than Just a Flaw

The ALBeast vulnerability is a significant oversight in AWS’s ALB authentication process, which could allow attackers to bypass security protocols and gain unauthorized access to sensitive applications. “What makes this vulnerability particularly alarming is its widespread impact—potentially affecting thousands of applications that rely on AWS’s ALB feature,” declared Liad Eliyahu, research lead at Miggo .

Eliyahu didn’t mince words when describing the gravity of the situation. “We uncovered a flaw that exposes a vast number of applications to potential exploitation unless immediate actions are taken by developers to secure their code,” he emphasized. His team’s thorough investigation revealed that the issue was not a one-off but a systemic problem, reflecting broader challenges within cloud security frameworks.

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Oprah endorses kamala harris at dnc: her political history—and rare endorsements—explained, bill clinton takes crack at trump’s hannibal lecter references in dnc speech, the shared responsibility model: a double-edged sword.

At the heart of this issue lies the shared responsibility model—a concept that has become a cornerstone of cloud security. In theory, this model clearly delineates the roles of cloud providers and their customers: providers like AWS are responsible for securing the infrastructure, while customers are responsible for securing their data and applications within that infrastructure. However, as the ALBeast flaw demonstrates, the practical application of this model is fraught with challenges.

“The complexity of the shared responsibility model has grown as cloud services have evolved,” explained Miggo’s CEO, Daniel Shechter . He highlighted that AWS, like other cloud providers, offers increasingly sophisticated services that blur the lines between provider and customer responsibilities.

In the case of ALBeast, the vulnerability arose not just from a misconfiguration but from a deeper integration of AWS’s application-layer services into customers’ own codebases. This integration creates a more intricate security landscape, where customers must not only configure their environments correctly but also ensure that their code is resilient to potential vulnerabilities introduced by third-party services.

Proactive Security: A Necessity, Not a Choice

One of the key lessons from the ALBeast discovery is the critical importance of proactive security measures. In today’s fast-paced digital environment, waiting for a breach to occur before taking action is a recipe for disaster. Shechter’s experience with ALBeast underscores this point: “The speed at which attackers can exploit vulnerabilities far outpaces the speed at which patches can be applied. Companies need to be ahead of the curve, not behind it.”

Miggo’s response to the ALBeast vulnerability is a model of proactive security in action. Rather than sitting back and waiting for the vulnerability to be exploited, Miggo took the initiative to reach out to potentially affected companies, offering guidance on how to mitigate the risk. This approach not only helped to prevent potential breaches but also highlighted the critical role that security vendors can play in supporting their customers through timely and effective communication.

The Future of Cloud Security: Collaboration is Key

The ALBeast flaw is more than just a technical vulnerability; it is a wake-up call for the entire cloud industry. As cloud services become more integral to business operations, the need for greater collaboration between providers and customers becomes increasingly apparent. The shared responsibility model, while useful, must evolve to reflect the realities of today’s complex cloud environments.

Shechter advocates for a more integrated approach to cloud security, where providers like AWS take a more active role in ensuring that their services are secure by design. “We need to move beyond the notion that customers are solely responsible for securing their applications,” Shechter argued. “Cloud providers must work more closely with their customers to identify and mitigate potential risks before they become critical vulnerabilities.”

This call for greater collaboration is not just about preventing the next ALBeast; it’s about fostering a culture of continuous improvement in cloud security . As Shechter pointed out, “Applications have changed dramatically over the past five years, but many companies’ security practices have not kept pace. It’s time for the industry to step up and address these gaps.”

A Call to Action

The discovery of the ALBeast vulnerability is a clear reminder that cloud security is a shared responsibility—but one that requires more than just a passive adherence to best practices. It demands proactive engagement, continuous collaboration, and a willingness to adapt to the ever-changing threat landscape.

For companies leveraging cloud services, the lesson is clear: don’t wait for vulnerabilities to be exposed. Take the initiative to understand the complexities of the shared responsibility model, stay vigilant in your security practices, and work closely with your cloud providers to ensure that your applications remain secure.

“This type of misconfiguration isn’t new; similar issues have been seen in other cloud services where improper token validation or lax source verification leads to security gaps,” summed up Soroko. “Security teams should ensure that their apps properly verify tokens and restrict traffic to only trusted sources, particularly their ALB.”

As we move forward, the ALBeast flaw should serve as a catalyst for a broader conversation about the future of cloud security—one that emphasizes the importance of collaboration, innovation, and a proactive approach to safeguarding our digital environments.

Update, August 21, 2024: This story has been updated with comments from Jason Soroko, senior vice president of product at Sectigo .

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