A fast genetic algorithm for a critical protection problem in biomedical supply chain networks |
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Affiliation: | 1. Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA 01003, United States;2. HEC Paris, Operations Mgmt. and Information Technology Dept., France;1. School of Business, Suzhou University of Science and Technology, No. 1, Ke Rui Road, New District, Suzhou 215009, PR China;2. College of Business, Shanghai University of Finance and Economics, No. 777, Guo Ding Road, Yang Pu District, Shanghai 200433, PR China;1. Division of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India;2. Complex Systems Research Centre, Shanxi University, Taiyuan, Shanxi, China |
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Abstract: | In this paper, we present a new bilevel model for a biomedical supply chain network with capacity and budget constraint due to the protection and interdiction operations. The components considered in this model are biomedical devices, distribution centers (DCs), medical suppliers (MSs), and hospitals and patients as the demand points. On the other hand, two levels of decisions in the network planning is suggested: (1) the defender’s decision about protection operations of MSs and DCs, the assignment of clients to the DCs, and quantity of products shipped to DCs from MSs to minimize the demand-weighted traveling costs and transport costs and (2) the attacker’s decision about interdiction operations of MSs and DCs to maximize the capacity or service reduction and losses. Because of nondeterministic polynomial time (NP)-hardness of the problem under consideration, an efficient and fast approach based on a genetic algorithm and a fast branch and cut method (GA–FBC) was developed to solve the proposed model. Also, the efficiency via the comparison of results with the genetic algorithm based on CPLEX (GA-CPLEX) and decomposition method (DM) is investigated. In order to assess the performance of the presented GA–FBC, a set of 27 instances of the problem is used. Comprehensive analysis indicates that the proposed approach significantly solves the problem. In addition, the benefits and advantages of preference with running times and its accuracy is shown numerically. Simulation results clearly demonstrate that the defender’s objective effectively reduced and CPU time also within the large-sized instances of the problem in comparison with the GA-CPLEX and DM. |
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Keywords: | Protection strategy Interdiction planning Biomedical supply chain Genetic algorithm Fast approach |
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