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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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Published in:Scientific World Journal pp. 595902 - 595903
Main Authors: Chiang, Tzu-An, Che, Z H, Cui, Zhihua
Format: research Journal Article
Published: Wiley-Blackwell 2014
Online Access:View this record in EBSCOhost
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      dt: 2014
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2014/595902
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        atl: Designing a multistage supply chain in cross-stage reverse logistics environments: application of particle swarm optimization algorithms.
      aug:
        au:
          Chiang, Tzu-An
          Che, Z H
          Cui, Zhihua
        affil: Department of Business Administration, National Taipei College of Business, Taipei 10051, Taiwan.
      sug:
        subj:
          Particle Swarm Optimization
          Consumer Satisfaction Statistics and Numerical Data
          Study Design
          Computer Simulation
          Consumer Satisfaction Economics
          Human
          Marketing Economics
          Marketing Methods
          Reproducibility of Results
          Transportation Economics
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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