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Evaluating Reverse Logistics Networks with Centralized Centers: An Adaptive Genetic Algorithm Approach Based on Fuzzy Logic Controller

Received: 16 January 2015     Accepted: 19 January 2015     Published: 8 February 2015
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Abstract

This paper proposes an adaptive genetic algorithm (FLC-aGA) approach based on fuzzy logic controller (FLC) for evaluating the reverse logistics (RL) networks with centralized centers. For the FLC-aGA approach, an adaptive scheme using a fuzzy logic controller is applied to GA loop. Five components which are composed of customers, collection centers, recovery centers, redistribution centers, and secondary markets are used to design the RL networks. For the RL with centralized centers (RLCC), collection center, recovery center, redistribution center and secondary market will be opened alone. The RLCC will be formulated as a mixed integer programming (MIP) model and its objective function is to minimize the total cost of unit transportation costs, fixed costs, and variable costs under considering various constraints. The MIP model for the RLCC is solved by using the FLC-aGA approach. Three test problems with various sizes of collection centers, recovery centers, redistribution centers, and secondary markets are considered and they are compared the FLC-aGA approach with other competing approaches. Finally, the optimal solutions by the FLC-aGA and other competing approaches are demonstrated each other using some measures of performance.

Published in International Journal of Intelligent Information Systems (Volume 4, Issue 2-1)

This article belongs to the Special Issue Logistics Optimization Using Evolutionary Computation Techniques

DOI 10.11648/j.ijiis.s.2015040201.15
Page(s) 25-38
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Adaptive Genetic Algorithm, Fuzzy Logic Controller (FLC), Reverse Logistics Network, Centralized Centers

References
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  • APA Style

    YoungSu Yun. (2015). Evaluating Reverse Logistics Networks with Centralized Centers: An Adaptive Genetic Algorithm Approach Based on Fuzzy Logic Controller. International Journal of Intelligent Information Systems, 4(2-1), 25-38. https://doi.org/10.11648/j.ijiis.s.2015040201.15

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    ACS Style

    YoungSu Yun. Evaluating Reverse Logistics Networks with Centralized Centers: An Adaptive Genetic Algorithm Approach Based on Fuzzy Logic Controller. Int. J. Intell. Inf. Syst. 2015, 4(2-1), 25-38. doi: 10.11648/j.ijiis.s.2015040201.15

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    AMA Style

    YoungSu Yun. Evaluating Reverse Logistics Networks with Centralized Centers: An Adaptive Genetic Algorithm Approach Based on Fuzzy Logic Controller. Int J Intell Inf Syst. 2015;4(2-1):25-38. doi: 10.11648/j.ijiis.s.2015040201.15

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  • @article{10.11648/j.ijiis.s.2015040201.15,
      author = {YoungSu Yun},
      title = {Evaluating Reverse Logistics Networks with Centralized Centers: An Adaptive Genetic Algorithm Approach Based on Fuzzy Logic Controller},
      journal = {International Journal of Intelligent Information Systems},
      volume = {4},
      number = {2-1},
      pages = {25-38},
      doi = {10.11648/j.ijiis.s.2015040201.15},
      url = {https://doi.org/10.11648/j.ijiis.s.2015040201.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2015040201.15},
      abstract = {This paper proposes an adaptive genetic algorithm (FLC-aGA) approach based on fuzzy logic controller (FLC) for evaluating the reverse logistics (RL) networks with centralized centers. For the FLC-aGA approach, an adaptive scheme using a fuzzy logic controller is applied to GA loop. Five components which are composed of customers, collection centers, recovery centers, redistribution centers, and secondary markets are used to design the RL networks. For the RL with centralized centers (RLCC), collection center, recovery center, redistribution center and secondary market will be opened alone. The RLCC will be formulated as a mixed integer programming (MIP) model and its objective function is to minimize the total cost of unit transportation costs, fixed costs, and variable costs under considering various constraints. The MIP model for the RLCC is solved by using the FLC-aGA approach. Three test problems with various sizes of collection centers, recovery centers, redistribution centers, and secondary markets are considered and they are compared the FLC-aGA approach with other competing approaches. Finally, the optimal solutions by the FLC-aGA and other competing approaches are demonstrated each other using some measures of performance.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - Evaluating Reverse Logistics Networks with Centralized Centers: An Adaptive Genetic Algorithm Approach Based on Fuzzy Logic Controller
    AU  - YoungSu Yun
    Y1  - 2015/02/08
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ijiis.s.2015040201.15
    DO  - 10.11648/j.ijiis.s.2015040201.15
    T2  - International Journal of Intelligent Information Systems
    JF  - International Journal of Intelligent Information Systems
    JO  - International Journal of Intelligent Information Systems
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    EP  - 38
    PB  - Science Publishing Group
    SN  - 2328-7683
    UR  - https://doi.org/10.11648/j.ijiis.s.2015040201.15
    AB  - This paper proposes an adaptive genetic algorithm (FLC-aGA) approach based on fuzzy logic controller (FLC) for evaluating the reverse logistics (RL) networks with centralized centers. For the FLC-aGA approach, an adaptive scheme using a fuzzy logic controller is applied to GA loop. Five components which are composed of customers, collection centers, recovery centers, redistribution centers, and secondary markets are used to design the RL networks. For the RL with centralized centers (RLCC), collection center, recovery center, redistribution center and secondary market will be opened alone. The RLCC will be formulated as a mixed integer programming (MIP) model and its objective function is to minimize the total cost of unit transportation costs, fixed costs, and variable costs under considering various constraints. The MIP model for the RLCC is solved by using the FLC-aGA approach. Three test problems with various sizes of collection centers, recovery centers, redistribution centers, and secondary markets are considered and they are compared the FLC-aGA approach with other competing approaches. Finally, the optimal solutions by the FLC-aGA and other competing approaches are demonstrated each other using some measures of performance.
    VL  - 4
    IS  - 2-1
    ER  - 

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Author Information
  • Division of Management Administration, Chosun University, Gwangju, Korea

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