Integrated Decision Making to Determine the Optimal Order Quantity for Raw Materials Using Genetic Evolutionary Algorithms

Abstract

Decision Making in economic organizations, especially the productivity, is one of the problems that researchers are interested in. It is important for the organizations that seek global competition and the integration of their decision will lead to coordination of decisions between the departments of these organizations to determine optimal order quantity and establish a correct inventory policy to warrant production with least total costs (costs of transportation, holding and purchasing), these is the objective of any company to achieve an adequate and enough inventory level to meet the future needs. In this paper, integrated decision making with three stages, first stage is forecasting with demands to final products using time series, second stage determine required quantities for raw material to manufacture these final products, and formulated a mathematical model reduces the total cost in third stage, when the transportation and purchase are variable costs with order quantity, while in just-in-time model or economic order quantity model are a fixed demand and purchase cost. The aim of this paper is integrated decision making to reduce total cost of required raw materials for the manufacturing processes and without and determine the optimal order quantity using genetic algorithms. This study was applied in Wasit company for cotton products in the textile factory, the results shown Holt-Winter method is the best method to forecasting because has least mean absolute error and the percentage of purchasing cost 73%, Transportation cost 8%, and holding cost 19%. The percentage of purchasing cost of cotton is biggest value, more 99% of purchasing cost.