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

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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[2] Fahimnia, B., Farahani, R. Z., Marian, R., & Luong, L. (2013). A review and critique on integrated production–distribution planning models and techniques. Journal of Manufacturing Systems, 32 (1), 1-19.
[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.
[4] Goldberg, D. E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Publishing Company, Reading, MA.
[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.
[8] Jakobs, S. (1996). On genetic algorithms for the packing of polygons. European journal of operational research, 88 (1), 165-181.
[9] Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220 (4598), 671-680.
[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.
[16] Yaghini, M., & Akhavan, R. (2012). Multicommodity network design problem in rail freight transportation planning. Procedia-Social and Behavioral Sciences, 43, 728-739.
[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.
Cite This Article
  • 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

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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
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    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
  • Department of Industrial Engineering, University of Science and Culture, Tehran, Iran

  • Department of Mathematics, University of Science and Culture, Tehran, Iran

  • Department of Industrial Engineering, University of Science and Technology, Tehran, Iran

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