A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management

Setareh Abedinzadeh; Hamid Reza Erfanian; Mojtaba Arabmomeni; Roya Soltani

doi:doi:10.11648/j.ijtam.20170306.16

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A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management

Setareh Abedinzadeh, Hamid Reza Erfanian, Mojtaba Arabmomeni, Roya Soltani

Published in International Journal of Theoretical and Applied Mathematics (Volume 3, Issue 6)

Received: 27 September 2016 Accepted: 5 January 2017 Published: 18 December 2017

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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.

Published in	International Journal of Theoretical and Applied Mathematics (Volume 3, Issue 6)
DOI	10.11648/j.ijtam.20170306.16
Page(s)	210-218
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), 2017. Published by Science Publishing Group

Keywords

Integrated Production-Distribution Planning, Supply Chain Management, Differential Evolution Algorithm, Scheduling, Vehicle Routing Problem

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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APA Style

Setareh Abedinzadeh, Hamid Reza Erfanian, Mojtaba Arabmomeni, Roya Soltani. (2017). A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management. International Journal of Theoretical and Applied Mathematics, 3(6), 210-218. https://doi.org/10.11648/j.ijtam.20170306.16

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ACS Style

Setareh Abedinzadeh; Hamid Reza Erfanian; Mojtaba Arabmomeni; Roya Soltani. A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management. Int. J. Theor. Appl. Math. 2017, 3(6), 210-218. doi: 10.11648/j.ijtam.20170306.16

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AMA Style

Setareh Abedinzadeh, Hamid Reza Erfanian, Mojtaba Arabmomeni, Roya Soltani. A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management. Int J Theor Appl Math. 2017;3(6):210-218. doi: 10.11648/j.ijtam.20170306.16

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@article{10.11648/j.ijtam.20170306.16,
  author = {Setareh Abedinzadeh and Hamid Reza Erfanian and Mojtaba Arabmomeni and Roya Soltani},
  title = {A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management},
  journal = {International Journal of Theoretical and Applied Mathematics},
  volume = {3},
  number = {6},
  pages = {210-218},
  doi = {10.11648/j.ijtam.20170306.16},
  url = {https://doi.org/10.11648/j.ijtam.20170306.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtam.20170306.16},
  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.},
 year = {2017}
}

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TY  - JOUR
T1  - A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management
AU  - Setareh Abedinzadeh
AU  - Hamid Reza Erfanian
AU  - Mojtaba Arabmomeni
AU  - Roya Soltani
Y1  - 2017/12/18
PY  - 2017
N1  - https://doi.org/10.11648/j.ijtam.20170306.16
DO  - 10.11648/j.ijtam.20170306.16
T2  - International Journal of Theoretical and Applied Mathematics
JF  - International Journal of Theoretical and Applied Mathematics
JO  - International Journal of Theoretical and Applied Mathematics
SP  - 210
EP  - 218
PB  - Science Publishing Group
SN  - 2575-5080
UR  - https://doi.org/10.11648/j.ijtam.20170306.16
AB  - 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.
VL  - 3
IS  - 6
ER  -

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Author Information

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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Plain Text BibTeX RIS

APA Style

Setareh Abedinzadeh, Hamid Reza Erfanian, Mojtaba Arabmomeni, Roya Soltani. (2017). A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management. International Journal of Theoretical and Applied Mathematics, 3(6), 210-218. https://doi.org/10.11648/j.ijtam.20170306.16

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ACS Style

Setareh Abedinzadeh; Hamid Reza Erfanian; Mojtaba Arabmomeni; Roya Soltani. A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management. Int. J. Theor. Appl. Math. 2017, 3(6), 210-218. doi: 10.11648/j.ijtam.20170306.16

Copy | Download

AMA Style

Setareh Abedinzadeh, Hamid Reza Erfanian, Mojtaba Arabmomeni, Roya Soltani. A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management. Int J Theor Appl Math. 2017;3(6):210-218. doi: 10.11648/j.ijtam.20170306.16

Copy | Download

@article{10.11648/j.ijtam.20170306.16,
  author = {Setareh Abedinzadeh and Hamid Reza Erfanian and Mojtaba Arabmomeni and Roya Soltani},
  title = {A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management},
  journal = {International Journal of Theoretical and Applied Mathematics},
  volume = {3},
  number = {6},
  pages = {210-218},
  doi = {10.11648/j.ijtam.20170306.16},
  url = {https://doi.org/10.11648/j.ijtam.20170306.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtam.20170306.16},
  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.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - A Differential Evolution Heuristic for Integrated Production-Distribution Scheduling in Supply Chain Management
AU  - Setareh Abedinzadeh
AU  - Hamid Reza Erfanian
AU  - Mojtaba Arabmomeni
AU  - Roya Soltani
Y1  - 2017/12/18
PY  - 2017
N1  - https://doi.org/10.11648/j.ijtam.20170306.16
DO  - 10.11648/j.ijtam.20170306.16
T2  - International Journal of Theoretical and Applied Mathematics
JF  - International Journal of Theoretical and Applied Mathematics
JO  - International Journal of Theoretical and Applied Mathematics
SP  - 210
EP  - 218
PB  - Science Publishing Group
SN  - 2575-5080
UR  - https://doi.org/10.11648/j.ijtam.20170306.16
AB  - 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.
VL  - 3
IS  - 6
ER  -

Copy | Download