scholarly journals Bi-objective integrated supply chain design with transportation choices: A multi-objective particle swarm optimization

2017 ◽  
Vol 13 (5) ◽  
pp. 1-26
Author(s):  
Xia Zhao ◽  
◽  
Jianping Dou ◽  
◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Particle Swarm ◽  
Supply Chain Design ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Integrated Supply Chain

scholarly journals Designing a Multistage Supply Chain in Cross-Stage Reverse Logistics Environments: Application of Particle Swarm Optimization Algorithms

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Tzu-An Chiang ◽  
Z. H. Che ◽  
Zhihua Cui
Keyword(s):  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Objective Function ◽  
Reverse Logistics ◽  
Particle Swarm ◽  
Supply Chain Design ◽  
Reverse Supply Chain ◽  
Swarm Optimization ◽  
Objective Function Values

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),VMaxmethod (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.

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