Particle Swarm Optimization Based Genetic Algorithm for Two-Stage Transportation Supply Chain

Authors: Siva Prasad Darla, C. D. Naiju, K. Annamalai, S. S. Rajiv Sushanth

Abstract:

Supply chain consists of all stages involved, directly or indirectly, includes all functions involved in fulfilling a customer demand. In two stage transportation supply chain problem, transportation costs are of a significant proportion of final product costs. It is often crucial for successful decisions making approaches in two stage supply chain to explicit account for non-linear transportation costs. In this paper, deterministic demand and finite supply of products was considered. The optimized distribution level and the routing structure from the manufacturing plants to the distribution centres and to the end customers is determined using developed mathematical model and solved by proposed particle swarm optimization based genetic algorithm. Numerical analysis of the case study is carried out to validate the model.

Keywords: Genetic Algorithm, Particle Swarm Optimization, Production, Remanufacturing

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329595

References:

[1] A. Cakravastia, I. S. Toha, N. Nakamura, "A two-stage model for the design of supply chain networks," Int. J. Production Economics, vol.80, pp.231-248, 2002.
[2] Jianming. Yao, Liwen. Liu, "Optimization analysis of supply chain scheduling in mass customization," International Journal of Production Economics, vol.60, pp.445-459, 2008.
[3] A. S. Crooml, P. Romano, M. Giannakis, "Supply Chain Management: an analytical framework for critical literature review," European Journal of Purchasing & Supply Management, vol. 6, pp.67-83, 2006.
[4] N. Jawahar, A.N. Balaji, "A genetic algorithm for the two-stage supply chain distribution problem associated with a fixed charge," European Journal of Operational Research, vol.194, pp.496-537, 2009.
[5] O.M. Akanle, D.Z. Zhang, "Agent-based model for optimizing supply chain configurations," International Journal of Production Economics, vol.115, pp.444- 460, 2008.
[6] I. Karaoglan, F. Altiparmak, M. Gen, L Lin, "A steady-state genetic algorithm for multi-product supply chain network design," Computers & Industrial Engineering, vol.102, pp.321-338, 2007.
[7] S. Pokharel, "A two objective model for decision making in a supply chain," Int. J. Production Economics, vol.111, pp.378-388, 2007.
[8] Z.X. Guo, W.K. Wong, S.Y.S. Leung, J.T. Fan, S.F. Chan, "Genetic optimization of order scheduling with multiple uncertainties," Expert Systems with Applications, vol.35, pp.1788-1801, 2008.
[9] A. Gunasekaran, Eric W.T. Ngai, "Modeling and analysis of build-toorder supply chains," European Journal of Operational Research, vol. 195, pp. 319-334, 2008.
[10] A. Pan, S.Y.S. Leung, K.L. Moon, "Optimal reorder decision-making in the agent-based apparel supply chain," Expert Systems with Applications, vol. 221, pp. 281-297, 2008.
[11] P. Borisovsky, A. Dolgui, A. Eremeev, "Genetic algorithms for a supply management problem: MIP-recombination vs greedy decoder," European Journal of Operations Research, vol.195, pp.770-779, 2009.
[12] M. Gen, F. Altiparmak, L Lin, "A genetic algotihm for two-stage transportation problem using priority-based encoding," OR Spectrum, vol. 28, pp.3337-354, 2008.
[13] J.Kennedy, and R.C.Eberhart, "Particle swarm optimization", In Proceedings of the IEEE international conference on neural networks, vol. 4, pp. 1942-1948, NJ: IEEE Service Center, Piscataway, 1995.
[14] J. Kennedy, and W.Spears, "Matching algorithms to problems: An experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator." In Proceedings of the IEEE international conference on evolutionary computation, pp.78-83, Anchorage, Alaska, 1998.
[15] J.Robinson, S. Sinton, and Y. Rahmat-Samii, "Particle swarm, genetic algorithm and their hybrids: Optimization of a profiled corrugated horn antenna." In IEEE Antennas and Propagation Society International Symposium, vol. 1, pp. 314-317, San Antonio, TX, June 16-21.