With increasing competitiveness in the business world, focus on supply chain has got greater attention. Therefore supply chain has to be made effective by reducing unnecessary losses occurring in the supply network. These losses are caused due to production, distribution planning and improper routing of vehicles in supply chain networks. The objective of this paper is to reduce costs across the supply chain by effectively allocating distribution centers to warehouses, reducing transportation costs and inventory costs. A nontraditional optimization tool that can effectively find good solutions to difficult combinatorial problems is Particle swarm optimization (PSO). Particle swarm optimization depicts the intelligence in swarm and flocking of swarms. The flocking depicts the information in the form of position and velocity. The clustering is done by calculating the distance from warehouse to various distribution centers which are assigned to the respective warehouses for distribution. The position and velocity of swarms are developed based on the distance matrix given. This lays platform to manage the supply chain optimally. Constraints were imposed on the routes traversed by the swarms. The constraints given are warehouse capacity and the distance to be traveled by swarm.
Keywords: Supply chain network, Particle Swarm Optimization, Transportation cost.
[...] Wafa, B.E.A., Haitham, M.S.L., Imad, M.A., and Khawla, Supply chain optimization of petroleum organization under uncertainty in market demands and prices”, European Journal of Operational Research 189 (2008) pp 822–840 Jeff, F., and John, W., “Chemical supply chain network optimization”, Computers and Chemical Engineering 32 (2008) pp 2481–2504 Reynolds,C., "Flocks, Herds, and Schools: A Distributed Behavioral Model", Compter Graphics, Vol.21, No.4, pp.25- Kennedy, J., and Eberhart, R., "Particle Swarm Optimization", Proceedings of IEEE International Conference on Neural Networks (ICNN'95), Vol. [...]
[...] The fitness value obtained from the above rank is optimized to obtain least cost The formula for calculating the position and velocity of the particle are as follows, vid + = vid ) + c1 * rand ( pid xid + c 2 * rand ( p gd xid . xid + = xid ) + vid + where, vid pid pgd xid c1 & c2 rand w - . (Eberhart et al., velocity of dimension d of the ith particle best previous position of the ith particle is the best position of the neighbors current position of the ith particle are acceleration constants random function in the range Inertia weight The inertia weight is 1.5 and the acceleration constant is c1 = c2 = 2 (Ying et al. [...]
[...] Reynolds has given the behavioral model for flocking of swarms, herds and schooling of fishes. Kennedy and Eberhart developed and presented particle swarm optimization (PSO) depicting the behavior of flocking swarms. Kennedy and Eberhart has formulated and published the intelligence in swarm. Shi et al. has presented a novel method based on PSO for traveling salesman problem. An uncertain strategy is added in the approach for optimizing the TSP and GTSP (Generalized traveling salesman problem). Zhao et al. has shown the interfacing of PSO for applications. [...]
[...] 12] proposed the optimization technique involving the behaviour of ants. Here the foraging behavior of ants for food using a pheromone substance is derived experimentally. Dorigo et al. proposed a new meta heuristic in ant colony optimization. It has been applied to traveling salesman problem and adaptive routing of communication networks. Wang discussed the partner selection and the production–distribution planning in supply chain network system. Besides the cost of production and transportation, the reliability of the structure and the unbalance of this system caused by the losses of production are considered. [...]
[...] Proceedings of the 1999 Congress on Volume Issue Page(s): - 1477 Vol Wang, H.S., two-phase ant colony algorithm for multi-echelon defective supply chain network design”, European Journal of Operational Research 192 (2009) pp 243–252. Sensi, G.D, Longo, F., Mirabelli, G., and Papoff, E., “Ants colony system for supply chain routes optimization”, Harbour, Maritime & Multimodal Logistics Modeling and Simulation MISS Spain- Barcelona Spain, 4-6 October Nikolaos, V.K, Georgios, K., Ioannis, A., and Vassili, L., Proceeding of the 19th European Conference on Modeling and Simulation (ECMS), Riga, Latvia Hu, X., Eberhart, R. C., and Shi, Y. “Engineering optimization with particle swarm”, Proceedings of the IEEE Swarm Intelligence Symposium 2003 [...]
using our reader.