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Co-reconfiguration of product family and supply chain using leader–follower Stackelberg game theory: Bi-level multi-objective optimization

Pakseresht, M ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1016/j.asoc.2020.106203
  3. Publisher: Elsevier Ltd , 2020
  4. Abstract:
  5. This article deals with product Family: a group of products with similar modules produced based on assembling to order (ATO) approach to cover diverse customer needs. The demand level and the customer requirements of these products are dynamically changing, which necessitate a novel model for co-reconfiguration of the product family (PF) and the supply chain (SC). Therefore, this article aims to apply Leader–follower Stackelberg Game Theory in order to present a co-reconfiguration of PF and SC based on three objectives: maximizing the total profit, maximizing customer utility and minimizing the supply chain cost in a bi-level structure. Maximizing the total profit and maximizing customer utility are the two objectives of the Leader problem for product family reconfiguration, which results in the optimal selection of components, modules, and product variants. The upper-level problem is considered as a multi-objective problem with the aforementioned two objectives. The lower level of this problem intends to reconfigure the supply chain with the objective of minimizing the supply chain costs, and therefore, to reach the optimal selection of suppliers, manufacturers, assembly plant, distribution centers, and retailers. A bi-level multi-objective linear programming problem (B-MOLP) is used to model the game of the leader–follower. A new particle swarm optimization algorithm, called bi-level multi-objective PSO (B-MOPSO), is developed to solve the proposed bi-level multi-objective model. To show the validity of the proposed model and the efficiency of our algorithm, a case study at a mountain bike industry is investigated. Finally, results in some managerial implications are obtained through sensitivity analysis. © 2020 Elsevier B.V
  6. Keywords:
  7. MOPSO algorithm ; Product family ; Supply chain ; Costs ; Game theory ; Genetic algorithms ; Linear programming ; Particle swarm optimization (PSO) ; Profitability ; Sales ; Sensitivity analysis ; Supply chains ; Bi-level programming ; Managerial implications ; Multi-objective linear programming ; Multi-objective modeling ; New particle swarm optimization ; Product families ; Reconfiguration ; Stackelberg Games ; Multiobjective optimization
  8. Source: Applied Soft Computing Journal ; Volume 91 , 2020
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S1568494620301435