A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management

Setareh Abedinzadeh; Hamid Reza Erfanian; Mojtaba Arabmomeni

doi:doi:10.11648/j.ijtam.20170306.19

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A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management

Setareh Abedinzadeh, Hamid Reza Erfanian, Mojtaba Arabmomeni

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

Received: 27 September 2016 Accepted: 10 January 2017 Published: 14 January 2018

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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.

Published in	International Journal of Theoretical and Applied Mathematics (Volume 3, Issue 6)
DOI	10.11648/j.ijtam.20170306.19
Page(s)	229-238
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), 2018. Published by Science Publishing Group

Keywords

Integrated Production-Distribution Planning, Rail Transportation, Deteriorating Items, Scheduling, Hybrid Genetic Algorithm-Simulated Annealing

References

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[3]	Ghiami, Y., & Williams, T. (2015). A two-echelon production-inventory model for deteriorating items with multiple buyers. International Journal of Production Economics, 159, 233-240.
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[5]	Hajiaghaei-Keshteli, M., Aminnayeri, M., & Ghomi, S. F. (2014). Integrated scheduling of production and rail transportation. Computers & Industrial Engineering, 74, 240-256.
[6]	Hajiaghaei-Keshteli, M., & Aminnayeri, M. (2014). Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm. Applied Soft Computing, 25, 184-203.
[7]	Holland, J. H. (1975). Adaption in natural and artiﬁcial systems. Ann Arbor MI: The University of Michigan Press.
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[10]	Leung, T. W., Yung, C. H., & Troutt, M. D. (2001). Applications of genetic search and simulated annealing to the two-dimensional non-guillotine cutting stock problem. Computers & industrial engineering, 40 (3), 201-214.
[11]	Lundy, M., & Mees, A. (1986). Convergence of an annealing algorithm. Mathematical programming, 34 (1), 111-124.
[12]	Maihami, R., & Karimi, B. (2014). Optimizing the pricing and replenishment policy for non-instantaneous deteriorating items with stochastic demand and promotional efforts. Computers & Operations Research, 51, 302-312.
[13]	Pasandideh, S. H. R., Niaki, S. T. A., & Asadi, K. (2015). Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA. Information Sciences, 292, 57-74.
[14]	Priyan, S., & Uthayakumar, R. (2014). Two-echelon multi-product multi-constraint product returns inventory model with permissible delay in payments and variable lead time. Journal of Manufacturing Systems.
[15]	Saracoglu, I., Topaloglu, S., & Keskinturk, T. (2014). A genetic algorithm approach for multi-product multi-period continuous review inventory models. Expert Systems with Applications, 41 (18), 8189-8202.
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[17]	Zhang, J., Liu, G., Zhang, Q., & Bai, Z. (2015). Coordinating a supply chain for deteriorating items with a revenue sharing and cooperative investment contract. Omega, 56, 37-49.

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Setareh Abedinzadeh, Hamid Reza Erfanian, Mojtaba Arabmomeni. (2018). A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management. International Journal of Theoretical and Applied Mathematics, 3(6), 229-238. https://doi.org/10.11648/j.ijtam.20170306.19

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ACS Style

Setareh Abedinzadeh; Hamid Reza Erfanian; Mojtaba Arabmomeni. A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management. Int. J. Theor. Appl. Math. 2018, 3(6), 229-238. doi: 10.11648/j.ijtam.20170306.19

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AMA Style

Setareh Abedinzadeh, Hamid Reza Erfanian, Mojtaba Arabmomeni. A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management. Int J Theor Appl Math. 2018;3(6):229-238. doi: 10.11648/j.ijtam.20170306.19

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@article{10.11648/j.ijtam.20170306.19,
  author = {Setareh Abedinzadeh and Hamid Reza Erfanian and Mojtaba Arabmomeni},
  title = {A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management},
  journal = {International Journal of Theoretical and Applied Mathematics},
  volume = {3},
  number = {6},
  pages = {229-238},
  doi = {10.11648/j.ijtam.20170306.19},
  url = {https://doi.org/10.11648/j.ijtam.20170306.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtam.20170306.19},
  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.},
 year = {2018}
}

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TY  - JOUR
T1  - A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management
AU  - Setareh Abedinzadeh
AU  - Hamid Reza Erfanian
AU  - Mojtaba Arabmomeni
Y1  - 2018/01/14
PY  - 2018
N1  - https://doi.org/10.11648/j.ijtam.20170306.19
DO  - 10.11648/j.ijtam.20170306.19
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  - 229
EP  - 238
PB  - Science Publishing Group
SN  - 2575-5080
UR  - https://doi.org/10.11648/j.ijtam.20170306.19
AB  - 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.
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

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APA Style

Setareh Abedinzadeh, Hamid Reza Erfanian, Mojtaba Arabmomeni. (2018). A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management. International Journal of Theoretical and Applied Mathematics, 3(6), 229-238. https://doi.org/10.11648/j.ijtam.20170306.19

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ACS Style

Setareh Abedinzadeh; Hamid Reza Erfanian; Mojtaba Arabmomeni. A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management. Int. J. Theor. Appl. Math. 2018, 3(6), 229-238. doi: 10.11648/j.ijtam.20170306.19

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AMA Style

Setareh Abedinzadeh, Hamid Reza Erfanian, Mojtaba Arabmomeni. A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management. Int J Theor Appl Math. 2018;3(6):229-238. doi: 10.11648/j.ijtam.20170306.19

Copy | Download

@article{10.11648/j.ijtam.20170306.19,
  author = {Setareh Abedinzadeh and Hamid Reza Erfanian and Mojtaba Arabmomeni},
  title = {A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management},
  journal = {International Journal of Theoretical and Applied Mathematics},
  volume = {3},
  number = {6},
  pages = {229-238},
  doi = {10.11648/j.ijtam.20170306.19},
  url = {https://doi.org/10.11648/j.ijtam.20170306.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtam.20170306.19},
  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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management
AU  - Setareh Abedinzadeh
AU  - Hamid Reza Erfanian
AU  - Mojtaba Arabmomeni
Y1  - 2018/01/14
PY  - 2018
N1  - https://doi.org/10.11648/j.ijtam.20170306.19
DO  - 10.11648/j.ijtam.20170306.19
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  - 229
EP  - 238
PB  - Science Publishing Group
SN  - 2575-5080
UR  - https://doi.org/10.11648/j.ijtam.20170306.19
AB  - 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.
VL  - 3
IS  - 6
ER  -

Copy | Download