Aggregate Planning Based On Stochastic Demand DEA Model With An Application In Production Planning

Production planning is a complicated and accurate process, which is significantly crucial for the development and competition of firms. There are many facts that will affect production planning, including inventories, market demand and raw materials. Firms are usually aiming to maximize profits while minimize costs, and this brings linear optimization into our consideration.Production planning refers to the fund allocation plan, that aims to maximum profits or minimum costs according to the known quantity of each node of the forecasting demand, to decide the production quantity, total number of employees, the number of inventory and the quantity of raw materials at different time periods. Market demand and inventory is usually known in traditional DEA-based resource allocation method. However, market demand is always changing according with the market discipline. Thus, it is not rigorous to view market demand as constant. To cope with the uncertain demand, we further develop our mathematical model and impose the normality assumption for the stochastic product demands. In the end, a numerical example with hypothetical production data is used to illustrate the model.This paper includes the following parts. Chapter1:introduction of research backgrounds and related definitions. Our research background is discussing the importance and utility of dynamic resource allocation in production planning, and that market demand is changing while the market is changing. Chapter2:First, we briefly introduce the definition and basic model of DEA. Second, we summarize the situation of current global studies, based on the research direction of this paper. Chapter3:This chapter mainly discusses the dynamic resources allocation and the production planning under the condition of stochastic demand. This chapter assumes that demand is the stochastic variable that obeys normal distribution. Chapter4:This chapter summarizes the whole paper and gives some advice of production planning for firms. In addition, this chapter concludes the shortcomings of this paper, and points out the direction for future studies.