A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management
International Journal of Theoretical and Applied Mathematics
Volume 3, Issue 6, December 2017, Pages: 210-218
Received: Sep. 27, 2016; Accepted: Jan. 5, 2017; Published: Dec. 18, 2017
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Authors
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
Roya Soltani, Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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Abstract
A supply chain may be considered as an integrated process in which a group of several organizations, work together. The two core optimization problems in a supply chain are production and distribution planning. In this research, we develop an integrated production-distribution (P-D) model. The problem is formulated as a mixed integer programming (MIP) model, which could then be solved using GAMS optimization software. A differential evolution (DE) algorithm is applied to solve large-sized MIP models. To the best of our knowledge, it is the first paper which applied DE algorithm to solve the integrated (P-D) planning models in supply chain management (SCM). The solutions obtained by GAMS are compared with those obtained from DE and the results show that DE is efficient in terms of computational time and the quality of solutions obtained.
Keywords
Integrated Production-Distribution Planning, Supply Chain Management, Differential Evolution Algorithm, Scheduling, Vehicle Routing Problem
To cite this article
Setareh Abedinzadeh, Hamid Reza Erfanian, Mojtaba Arabmomeni, Roya Soltani, A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management, International Journal of Theoretical and Applied Mathematics. Vol. 3, No. 6, 2017, pp. 210-218. doi: 10.11648/j.ijtam.20170306.16
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.
References
[1]
Min, H. and G. Zhou, Supply chain modeling: past, present and future. Computers & industrial engineering, 2002. 43 (1): p. 231-249.
[2]
Barbarosoğlu, G. and D. Özgür, Hierarchical design of an integrated production and 2-echelon distribution system. European Journal of Operational Research, 1999. 118 (3): p. 464-484.
[3]
Park, B. J., H. R. Choi, and M. H. Kang, Integration of production and distribution planning using a genetic algorithm in supply chain management, in Analysis and Design of Intelligent Systems using Soft Computing Techniques. 2007, Springer. p. 416-426.
[4]
Fahimnia, B., et al., A review and critique on integrated production–distribution planning models and techniques. Journal of Manufacturing Systems, 2013. 32 (1): p. 1-19.
[5]
Pyke, D. F. and M. A. Cohen, Performance characteristics of stochastic integrated production-distribution systems. European Journal of Operational Research, 1993. 68 (1): p. 23-48.
[6]
Lee, Y. H. and S. H. Kim. Optimal production-distribution planning in supply chain management using a hybrid simulation-analytic approach. in Proceedings of the 32nd conference on Winter simulation. 2000. Society for Computer Simulation International.
[7]
Lee, Y. H. and S. H. Kim, Production–distribution planning in supply chain considering capacity constraints. Computers & Industrial Engineering, 2002. 43 (1): p. 169-190.
[8]
Rizk, N., A. Martel, and S. D’Amours, Multi-item dynamic production-distribution planning in process industries with divergent finishing stages. Computers & operations research, 2006. 33 (12): p. 3600-3623.
[9]
Nishi, T., M. Konishi, and M. Ago, A distributed decision making system for integrated optimization of production scheduling and distribution for aluminum production line. Computers & chemical engineering, 2007. 31 (10): p. 1205-1221.
[10]
Farahani, R. Z. and M. Elahipanah, A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. International Journal of Production Economics, 2008. 111 (2): p. 229-243.
[11]
Safaei, A., et al., Integrated multi-site production-distribution planning in supply chain by hybrid modelling. International Journal of Production Research, 2010. 48 (14): p. 4043-4069.
[12]
Storn, R. and K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 1997. 11 (4): p. 341-359.
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