Volume 4, Issue 2-1, March 2015, Page: 25-38
Evaluating Reverse Logistics Networks with Centralized Centers: An Adaptive Genetic Algorithm Approach Based on Fuzzy Logic Controller
YoungSu Yun, Division of Management Administration, Chosun University, Gwangju, Korea
Received: Jan. 16, 2015;       Accepted: Jan. 19, 2015;       Published: Feb. 8, 2015
DOI: 10.11648/j.ijiis.s.2015040201.15      View  2986      Downloads  102
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.
Keywords
Adaptive Genetic Algorithm, Fuzzy Logic Controller (FLC), Reverse Logistics Network, Centralized Centers
To cite this article
YoungSu Yun, Evaluating Reverse Logistics Networks with Centralized Centers: An Adaptive Genetic Algorithm Approach Based on Fuzzy Logic Controller, International Journal of Intelligent Information Systems. Special Issue: Logistics Optimization Using Evolutionary Computation Techniques. Vol. 4, No. 2-1, 2015, pp. 25-38. doi: 10.11648/j.ijiis.s.2015040201.15
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