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
Views 3478 Downloads 166
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.
Beamon, B. M. (1998). Supply chain design and analysis: Models and methods. International journal of production economics, 55 (3), 281-294.
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.
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.
Goldberg, D. E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Publishing Company, Reading, MA.
Hajiaghaei-Keshteli, M., Aminnayeri, M., & Ghomi, S. F. (2014). Integrated scheduling of production and rail transportation. Computers & Industrial Engineering, 74, 240-256.
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.
Holland, J. H. (1975). Adaption in natural and artiﬁcial systems. Ann Arbor MI: The University of Michigan Press.
Jakobs, S. (1996). On genetic algorithms for the packing of polygons. European journal of operational research, 88 (1), 165-181.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220 (4598), 671-680.
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.
Lundy, M., & Mees, A. (1986). Convergence of an annealing algorithm. Mathematical programming, 34 (1), 111-124.
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.
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.
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.
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.
Yaghini, M., & Akhavan, R. (2012). Multicommodity network design problem in rail freight transportation planning. Procedia-Social and Behavioral Sciences, 43, 728-739.
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.