Multi Product Inventory Optimization using Uniform Crossover Genetic Algorithm

Details Multi Product Inventory Optimization using Uniform Crossover Genetic Algorithm Multi Product Inventory Optimization using Uniform Crossover Genetic Algorithm

2010-02-10

arxiv.org/abs/1002.2195v1

International Journal of Computer Science and Information Security, IJCSIS, Vol. 7, No. 1, pp. 170-179, January 2010, USA

Computer Science

Neural and Evolutionary Computing

IEEE format, International Journal of Computer Science and Information Security, IJCSIS January 2010, ISSN 1947 5500, http:/

Scientific paper

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 percent 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. 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. This paper deals with the problem of determination of base stock levels in a ten member serial supply chain with multiple products produced by factories using Uniform Crossover Genetic Algorithms. The complexity of the problem increases when more distribution centers and agents and multiple products were involved. These considerations leading to very complex inventory management process has been resolved in this work.

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