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), 2024. Published by Science Publishing Group |
Integrated Production-Distribution Planning, Rail Transportation, Deteriorating Items, Scheduling, Hybrid Genetic Algorithm-Simulated Annealing
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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
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
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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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}
}
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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