Applying Genetic Algorithms for Inventory Lot-Sizing Problem with Supplier Selection under Storage Capacity

Abstract

Genetic algorithms are applied to the multiple products and multiple periods inventory lot-sizing problem with supplier selection under storage capacity. The objective of this research is to calculate the optimal inventory lot-sizing for each supplier and minimize the total inventory cost which includes joint purchase cost of the products, transaction cost for the suppliers, and holding cost for remaining inventory. It is assumed that demand of multiple products is known over a planning horizon. The problem is formulated as a mixed integer programming and is solved with genetic algorithms. Finally, numerical example is provided to illustrate the solution procedure. The results determine what products to order in what quantities with which suppliers in which periods.