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

YoungSu Yun

doi:doi:10.11648/j.ijiis.s.2015040201.15

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

YoungSu Yun

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

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

[1]	REVLOG (2004) Available at: http://www.fbk.eur.nl/OZ/REVLOG/PROJECTS/TERMINOLOGY/def-reverselogistics.html.
[2]	Min, H., Ko, H. J. & Ko, C. S. (2006). A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns, Omega, 34, 56-69.
[3]	Tuzkaya, G. & Gulsun, B. (2008). Evaluating centralized return centers in a reverse logistics network: An integrated fuzzy multi-criteria decision approach, International Journal of Environment Science Technology, 5(3), 339-352.
[4]	Lee, J. E., Gen, M. & Rhee, K. G. (2009). Network model and optimization of reverse logistics by hybrid genetic algorithm, Computers & Industrial Engineering, 56, 951-964.
[5]	Yun, Y. S., Gen, M. & Hwang, R. K. (2013). Adaptive genetic algorithm to multi-stage reverse logistics network design for product resale, Information: An International Interdisciplinary Journal, 15(12), 6117-6138.
[6]	Stock, J. K. (1992). Reverse logistics, White paper, Council of logistics management. IL: Oak Brook.
[7]	Kroon, L. & Vrijens, G. (1995). Returnable containers: An example of reverse logistics, International Journal of Physical Distribution & Logistics Management, 25(2), 56–68.
[8]	Barros, A., Dekker, I. & Scholten, V. A. (1998). A two-level network for recycling sand: A case study, European Journal of Operational Research, 110(2), 199–214.
[9]	Brito, M. P., Dekker, R. & Flapper, S. D. P. (2003). Reverse Logistics: a review of case studies, ERIM Report Series Reference No. ERS-2003-012-LIS, http://ssrn.com/abstract=411649.
[10]	Fleischmann, M., Bloemhof-Ruwaard, J. M., Dekker, R., Vander Laan, E., Van Numen, J. A. E. E. & Van Wassenhove, L. N. (1997). Quantitative models for reverse logistics: A review, European Journal of Operational Research., 103(1), 1-17.
[11]	Fleischmann, M., Krikke, H. R., Dekker, R. & Flapper, S. D. P. (2000). A characterization of logistics networks for product recovery, Omega, 28(6), 653-666.
[12]	Pati, R. K., Vrat, P. & Kumar, P. (2008). A goal programming model for paper recycling system, Omega, 36(3), 405–417.
[13]	Aras, N. & Aksen, D. (2008). Locating collection centers for distance- and incentive-dependent returns, International Journal of. Production Economics, 111(2), 316-333,
[14]	Savaskan, R. C., Bhattacharya, S. & Van Wassenhove, L. N. (2004). Closed-loop supply chain models with product remanufacturing, Management Science, 50, 239-252.
[15]	Gen, M. & Cheng, R. (1997). Genetic algorithms and engineering design, John Wiley & Son, New York.
[16]	Song, Y. H., Wang, G. S., Wang, P. T. & Johns, A. T. (1997). Environmental/economic dispatch using fuzzy logic controlled genetic algorithms. IEEE Proceedings on Generation, Transmission and Distribution, 144(4), 377-382.
[17]	Wu, Q. H., Cao, Y. J. & Wen, J. Y. (1998). Optimal reactive power dispatch using an adaptive genetic algorithm. Electrical Power and Energy Systems, 20(8), 563-69.
[18]	Gen, M. & Cheng, R. (2000). Genetic Algorithms and Engineering Optimization. New York: John-Wiley & Sons.
[19]	Mak, K. L., Wong, Y. S. & Wang, X. X. (2000). An adaptive genetic algorithm for manufacturing cell formation. International Journal of Manufacturing Technology, 16, 491-97.
[20]	Yun, Y. S. & Gen, M. (2003). Performance analysis of adaptive genetic algorithms with fuzzy logic and heuristics. Fuzzy Optimization and Decision Making, 2(2), 161-75.
[21]	Yun, Y. S. (2010). Reliability optimization problems using adaptive genetic algorithm and improved particle swarm optimization, Nova Science Publisher, Inc., New York,. pp. 1-34.
[22]	Srinivas, M. & L. M. Patnaik (1994). Adaptive probabilities of crossover and mutation in genetic algorithms, IEEE Transaction on Systems, Man and Cybernetics, 24(4), 656-667
[23]	Wang, P. T., G. S. Wang & Z. G. Hu (1997). Speeding up the search process of genetic algorithm by fuzzy logic, Proceedings of the 5th European Congress on Intelligent Techniques and Soft Computing, 665-671.
[24]	Hong, T. P., H. S. Wang, W. Y. Lin & W. Y. Lee, (2002). Evolution of appropriate crossover and mutation operators in a genetic process, Applied Intelligence, 16, 7-17.
[25]	Yun, Y. S., (2002). Genetic algorithm with fuzzy logic controller for preemptive and non-preemptive job shop scheduling problems, Computers and Industrial Engineering, 43(3), 623-644.
[26]	Yun, Y. S., M. Gen * S. L. Seo, (2003). Various hybrid methods based on genetic algorithm with fuzzy logic controller, Journal of Intelligent Manufacturing, 14(3-4), 401-419.
[27]	Subbu, R., A. C. Sanderson & P. P. Bonissone, (1988). Fuzzy logic controlled genetic algorithms versus tuned genetic algorithms: an agile manufacturing application, Proceedings of the 1999 IEEE International Symposium on Intelligent Control (ISIC), 434-440

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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
SP  - 25
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

YoungSu Yun

Division of Management Administration, Chosun University, Gwangju, Korea

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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}
}

Copy | Download

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
SP  - 25
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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