title: Hybrid algorithms for fuzzy reverse supply chain network design.
abstract: In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.
authors.affiliation: Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan.
authors.forename: Z H
authors.initials: ZH
authors.lastname: Che
authors.affiliation: Department of Business Administration, National Taipei College of Business, Taipei 10051, Taiwan.
authors.forename: Tzu-An
authors.initials: TA
authors.lastname: Chiang
authors.affiliation: Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan.
authors.forename: Y C
authors.initials: YC
authors.lastname: Kuo
authors.affiliation: Complex System and Computational Intelligent Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, China ; State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China.
authors.forename: Zhihua
authors.identifier: 0000-0001-7388-3180
authors.initials: Z
authors.lastname: Cui
mesh_terms: D000465:Algorithms; D017143:Fuzzy Logic; D008962:Models, Theoretical