A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management
International Journal of Theoretical and Applied Mathematics
Volume 3, Issue 6, December 2017, Pages: 229-238
Received: Sep. 27, 2016;
Accepted: Jan. 10, 2017;
Published: Jan. 14, 2018
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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
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.
Hamid Reza Erfanian,
A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management, International Journal of Theoretical and Applied Mathematics.
Vol. 3, No. 6,
2017, pp. 229-238.
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