Designing a multistage supply chain in cross-stage reverse logistics environments: application of particle swarm optimization algorithms.
This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), V(Max) method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did.
Department of Business Administration, National Taipei College of Business, Taipei 10051, Taiwan.
Tzu-An
TA
Chiang
Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan.
Z H
ZH
Che
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.
Zhihua
0000-0001-7388-3180
Z
Cui
D000465:Algorithms; D003198:Computer Simulation; D003258:Consumer Behavior / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D040541:Marketing / Q000191:economics / Q000379:methods; D015203:Reproducibility of Results; D012107:Research Design; D014186:Transportation / Q000191:economics