A Competitive Bilevel Programming Model for Green, CLSCs in Light of Government Incentives

• Published in: Journal of mathematics (Hidawi) Vol. 2024; pp. 1 - 28
• Main Authors: Rahmani, Arsalan, Hosseini, Meysam, Sahami, Amir
• Format: Journal Article
• Language: English
• Published: Cairo Hindawi 2024; Hindawi Limited
• Subjects: Algorithms; Benders decomposition; Clean technology; Closed loops; Decision making; Environmental protection; Heuristic methods; Incentives; Integer programming; Linear programming; Markets; Mixed integer; Particle swarm optimization; Supply chain management; Supply chains; Sustainability; United States > US
• ISSN: 2314-4629; 2314-4785
• DOI: 10.1155/2024/4866890

The growth of world population has fueled environmental, legal, and social concerns, making governments and companies attempt to mitigate the environmental and social implications stemming from supply chain operations. The state-run Environmental Protection Agency has initially offered financial incentives (subsidies) meant to encourage supply chain managers to use cleaner technologies in order to minimize pollution. In today’s competitive markets, using green technologies remains vital. In the present project, we have examined a class of closed-loop supply chain competitive facility location-routing problems. According to the framework of the competition, one of the players, called the Leader, opens its facilities first. The second player, called the Follower, makes its decision when Leader’s location is known. Afterwards, each customer chooses an open facility based on some preference huff rules before returning the benefits to one of the two companies. The follower, under the influence of the leader’s decisions, performs the best reaction in order to obtain the maximum capture of the market. So, a bilevel mixed-integer linear programming model is formulated. The objective function at both levels includes market capture profit, fixed and operating costs, and financial incentives. A metaheuristic quantum binary particle swarm optimization (PSO) is developed via Benders decomposition algorithm to solve the proposed model. To evaluate the convergence rate and solution quality, the method is applied to some random test instances generated in the literature. The computational results indicate that the proposed method is capable of efficiently solving the model.