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 prob...

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Bibliographic Details
Published in:TheScientificWorld 2014-01, Vol.2014 (2014), p.1-19
Main Authors: Chiang, Tzu-An, Cui, Zhihua, Che, Z. H.
Format: Article
Language:English
Subjects:
Algorithms
Computer Simulation
Consumer Behavior - economics
Consumer Behavior - statistics & numerical data
Defective products
Design
Humans
Linear programming
Logistics
Management
Manufacturing
Marketing - economics
Marketing - methods
Mathematical optimization
Optimization
Planning
Product returns
Production costs
Reproducibility of Results
Research Design
Reverse logistics
Suppliers
Supply chains
Swarm intelligence
Transportation - economics
Traveling salesman problem
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Summary: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.
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2014/595902