Volume 3, Issue 6, December 2017, Page: 229-238
A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management
Setareh Abedinzadeh, Department of Industrial Engineering, University of Science and Culture, Tehran, Iran
Hamid Reza Erfanian, Department of Mathematics, University of Science and Culture, Tehran, Iran
Mojtaba Arabmomeni, Department of Industrial Engineering, University of Science and Technology, Tehran, Iran
Received: Sep. 27, 2016;       Accepted: Jan. 10, 2017;       Published: Jan. 14, 2018
DOI: 10.11648/j.ijtam.20170306.19      View  2410      Downloads  80
Abstract
In this paper, we present an integrated production-distribution (P-D) model which considers rail transportation to move deteriorating items. The problem is formulated as a mixed integer programming (MIP) model, which could then be solved using GAMS optimization software. A hybrid genetic algorithm-simulated annealing (GA-SA) is developed to solve the real-size problems in a reasonable time period. The solutions obtained by GAMS are compared with those obtained from the hybrid GA-SA and the results show that the hybrid GA-SA is efficient in terms of computational time and the quality of the solution obtained.
Keywords
Integrated Production-Distribution Planning, Rail Transportation, Deteriorating Items, Scheduling, Hybrid Genetic Algorithm-Simulated Annealing
To cite this article
Setareh Abedinzadeh, Hamid Reza Erfanian, Mojtaba Arabmomeni, A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management, International Journal of Theoretical and Applied Mathematics. Vol. 3, No. 6, 2017, pp. 229-238. doi: 10.11648/j.ijtam.20170306.19
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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