Multi-factory, Multi-Product Inventory Optimization using Genetic Algorithm for Efficient Supply Chain Management

摘要

Inventory management is considered to be an important field in Supply Chain Management because the cost of inventories in a supply chain accounts for about 30% of the value of the product. The service provided to the customer eventually gets enhanced once the efficient and effective management of inventory is carried out all through the supply chain. Estimation of the precise amount of inventory at each point in the supply chain devoid of excesses and shortages despite minimizing the total supply chain cost is a chief concern for the inventory and supply chain managers. The precise estimation of optimal inventory is essential since shortage of inventory yields to lost sales, while excess of inventory may result in pointless storage costs. Thus the determination of the inventory to be held at various levels in a supply chain becomes inevitable so as to ensure minimal cost for the supply chain. The minimization of the total supply chain cost can only be achieved when optimization of the base stock level is carried out at each member of the supply chain. Genetic Algorithms is used to achieve inventory optimization in the supply chain. The complexity of the problem increases when more distribution centers and agents were involved. The consideration of multiple products and multiple factories leading to very complex inventory management process has been resolved in this work.