# Cost optimization of supply chain networks using particle swarm optimization

- Abstract
- Introduction
- Literature survey
- Methodology
- Results and discussions
- Conclusion
- References

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.

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[...] 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. [...]

[...] From the above discussion it is observed that particle swarm algorithm is successfully applied in various supply chain optimization problems. In this context, the present work attempts to solve the distribution planning activities in a two stage supply chain network METHODOLOGY The present work considers supply chain distribution network involving distribution of a single product across several distribution points through warehouses. Here the problem involves both location of warehouses and allocation of distribution centres to warehouses. If the location of warehouse is fixed, this problem will be simplified into traditional transportation problem. [...]