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

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.

[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.