Genetic algorithm for inventory lot-sizing with supplier selection under fuzzy demand and costs

Authors:
Jafar Rezaei;Mansoor Davoodi
Affiliations:
Department of Industrial Management, Vali-e-Asr University of Rafsanjan, Rafsanjan, Kerman, Iran;Department of Computer Sciences, Vali-e-Asr University of Rafsanjan, Rafsanjan, Kerman, Iran
Venue:
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Year:
2006

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Abstract

In this paper a multi-period inventory lot sizing scenario, where there are multiple products and multiple suppliers, is solved with a Real Parameter Genetic Algorithm. We assume that demand of multiple discrete products is known, not exactly, over a planning horizon and transaction cost is supplier dependent, but does not depend on the variety nor quantity of products involved and holding cost is product-dependent and there are no capacity restrictions and no backlogging is allowed. Because of uncertainties in demand and inventory costs, we consider demand and all costs as fuzzy numbers. The problem is formulated as a fuzzy mixed integer programming and then converted to equivalent crisp decision making problems and is solved with a Real Parameter Genetic Algorithm. 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.