A fast genetic algorithm for a critical protection problem in biomedical supply chain networks

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 hospital...

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Vydáno v:Applied soft computing Ročník 75; s. 162 - 179
Hlavní autoři: Khanduzi, Raheleh, Sangaiah, Arun Kumar
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.02.2019
Témata:
Biomedical supply chain
Fast approach
Genetic algorithm
Interdiction planning
Protection strategy
Interdiction planning
Genetic algorithm
Fast approach
Protection strategy
Biomedical supply chain
ISSN:1568-4946, 1872-9681
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Popis
Shrnutí: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. •A new bi-level fortification/interdiction problem is developed for a biomedical supply chain network.•An improved genetic algorithm by employing a fast branch and cut method (GA–FBC) is proposed to solve the considered problem.•The performance of the GA–FBC is compared with genetic algorithm based on CPLEX and decomposition method on several test instances of the problem.•It is found that the proposed GA–FBC provides more efficient solutions than other approaches regarding performance measures.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2018.11.010