A Competitive Bilevel Programming Model for Green, CLSCs in Light of Government Incentives
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 inc...
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| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2024/4866890 |
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| author | Arsalan Rahmani Meysam Hosseini Amir Sahami |
| author_facet | Arsalan Rahmani Meysam Hosseini Amir Sahami |
| author_sort | Arsalan Rahmani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-03c0d24665a24162b4cee8f6ee92ac11 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-03c0d24665a24162b4cee8f6ee92ac112025-02-03T01:29:31ZengWileyJournal of Mathematics2314-47852024-01-01202410.1155/2024/4866890A Competitive Bilevel Programming Model for Green, CLSCs in Light of Government IncentivesArsalan Rahmani0Meysam Hosseini1Amir Sahami2Department of MathematicsDepartment of MathematicsDepartment of MathematicsThe 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.http://dx.doi.org/10.1155/2024/4866890 |
| spellingShingle | Arsalan Rahmani Meysam Hosseini Amir Sahami A Competitive Bilevel Programming Model for Green, CLSCs in Light of Government Incentives Journal of Mathematics |
| title | A Competitive Bilevel Programming Model for Green, CLSCs in Light of Government Incentives |
| title_full | A Competitive Bilevel Programming Model for Green, CLSCs in Light of Government Incentives |
| title_fullStr | A Competitive Bilevel Programming Model for Green, CLSCs in Light of Government Incentives |
| title_full_unstemmed | A Competitive Bilevel Programming Model for Green, CLSCs in Light of Government Incentives |
| title_short | A Competitive Bilevel Programming Model for Green, CLSCs in Light of Government Incentives |
| title_sort | competitive bilevel programming model for green clscs in light of government incentives |
| url | http://dx.doi.org/10.1155/2024/4866890 |
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