Designing a Reverse Logistics Network for End-of-Life Vehicles Recovery

The environmental factors are receiving increasing attention in different life cycle stages of products. When a product reaches its End-Of-Life (EOL) stage, the management of its recovery process is affected by the environmental and also economical factors. Selecting efficient methods for the collection and recovery of EOL products has become an important issue. The European Union Directive 2000/53/EC extends the responsibility of the vehicle manufacturers to the postconsumer stage of the vehicle. In order to fulfill the requirements of this Directive and also efficient management of the whole recovery process, the conceptual framework of a reverse logistics network is presented. The distribution of new vehicles in an area and also collecting the End-of-Life Vehicles (ELVs) and their recovery are considered jointly. It is assumed that the new vehicles distributors are also responsible for collecting the ELVs. Then a mathematical model is developed which minimizes the costs of setting up the network and also the relevant transportation costs. Because of the complexity of the model, a solution methodology based on the genetic algorithm is designed which enables achieving good quality solutions in a reasonable algorithm run time

Effective league championship algorithm and lower bound procedure for scheduling a single batch-processing machine with non-identical job sizes and job rejection

We address the scheduling problem of a set of non-identical size jobs on a single batch-processing machine (SBPM) wherein the scheduler can make decision whether to schedule a job in batches or not to schedule it with a job-dependent penalty. The processing time of a batch is the greatest job processing time in that batch (parallel batching or p-batching). The scheduler wants to minimize a given objective function f, where f is the sum total of the rejection penalties of the rejected jobs (rejection cost) plus the makespan of the scheduled ones. We formulate the aforementioned problem as a 0-1 mixed integer programming model. We also apply an effective dynamic programming algorithm (DPA) to calculate a lower bound (LB) on the optimal cost of the problem. To tackle the problem, we propose a grouping algorithm, based on league championship algorithm (LCA), with new updating equations maintaining the major characteristics of the original updating equations of the LCA and well-suited to the structure of the problem. For small problems, performance of the proposed LCA is compared with GAMS/CPLEX solver. For large-scale instances, a genetic algorithm is adopted as a basis for comparison. Simulated experiments confirm the performance of the proposed methods

Revenue Management Under Variable Resource Capacity Requirements and Importance Factor for Patient Classes in Hospitals

Revenue Management (RM) is a subfield of Operations Research that aims at maximizing revenues acquired by selling products/services in a specified period to the right customers. In this paper we address the problem of optimal assignment of general surgery ward of operation room to different classes of surgeries, each of which requiring different surgery time, using revenue management mechanisms. Deciding about accepting or rejecting a surgery request is made beside the option of preserving capacity of the operation room for future valuable requests based on the adjusted revenue (via inclusion of importance levels derived from expert opinions by an AHP approach). Data in this study were collected from Sayad-e-Shirazi Hospital of GorGan city. For formulating the problem we develop contingency and deterministic dynamic programming algorithms. Besides, we also propose a heuristic method based on  a deterministic linear programming approach. The number of accepted requests for classes with higher financial priority using RM approach are compared with FCFS policy and results signify that a higher level of efficiency is attainable using the proposed RM approaches. The efficiency along with using different surgical importance levels in accepting requests is also investigated

Optimal resource allocation model in disaster situations for maximizing the value of operational process resiliency and continuity

Organizations need to apply resilience and business continuity in industry to protect themselves against the crises and destructive events. Also, the growing expansion of competition in the global market and the increasing crisis in the world have increased the importance of optimal resource allocation. With the optimal resource allocation, huge losses and damages to organizations are prevented. The problem of resource allocation can be raised alongside the criteria of process resilience and continuity. Therefore, organizations change their perspective from focusing solely on reducing vulnerability to increasing resilience and continuity against to accidents in crises and destructive situations. The objective of this paper is proposed a mathematical model for optimal resource allocation with the aim of minimizing the lack of process resilience and maximizing the process continuity. So, the organization can continue to operate with available resources in times of crisis and lack of resources. Also, main activities and processes are not interrupted by crises and destructive events. After solving the model using a case study (textile industry), the results of the model were described and it was found that destructive events were recovered before the tolerance threshold and crisis and destructive events did not interrupt activities and processes

Reliable cross-docking location problem under the risk of disruptions

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