A Binary Multi-Objective Genetic Algorithm & PSO involving Supply Chain Inventory Optimization with Shortages, inflation

ABSTRACT In this paper a supply chain inventory optimization model for shortages, inflation is considered under assumption that the Inventory cost including holding cost shortages and inflation. The demand and holding cost, both are taken as variable. Shortages are allowed in the inventory model and a fraction of shortages backlogged at the next replenishment cycle. Transportation cost is taken to be negligible and goods are transported on the basis of bulk release pattern. A four level supply chain consists of a production, distributor Transportation and retailer, who are the cost bearers. It is necessary to have a coordinated approach between the tiers so that the chain is timed accurately for least inventory and minimum cost consequently maximum profits. In this paper, we consider a coordinated four echelon supply chain with a single production supplying a single type of product to single distributor and then to a single retailer. In this paper, we propose an efficient approach that effectively utilizes the Multi-Objective Genetic Algorithm and Multi-Objective Particle Swarm Optimization Algorithm for optimal inventory control. This paper reports a method based on Multi-Objective Genetic Algorithm and Multi- Objective Particle Swarm Optimization Algorithm to optimize inventory in supply chain management. We focus specifically on determining the most probable excess stock level and shortage level required for inventory optimization in the supply chain so that the total supply chain cost will be minimized. A numerical example is presented to illustrate the model and sensitivity is performed for a parameter keeping rest unchanged.