Analysis of inventory lot size problem
Lot size inventory control problem with ambiguous variable demand is important in industry as inventories can be a major commitment of monetary resources and affect virtually every aspect of daily operations. Inventories can be an important competitive weapon and are a major control problem in many companies, and improper lot sizes can affect the inventory levels and the costs associated with them. In the present thesis, we formulate and analyze such a problem and its variations under both crisp and fuzzy environments with a variety of assumptions. Specifically, we model such a problem with imprecise variable demand and imprecise total cost that includes setup, backordering and holding costs, as zero-one integer linear program. Also, the problem is modeled as piece wise integer linear program by incorporating quantity discounts. Furthermore, we consider such a problem, with ambiguous variable demand to be satisfied as closely as possible. Therefore we use goal-programming approach to solve such a problem. The present thesis seeks to provide an alternate, easy to understand and hopefully improved means of obtaining optimal lot sizing solution. The thesis demonstrates the adaptability of the approaches used, for decision making in a variety of applications, particularly scheduling purchases and production. The methods presented in this thesis are computationally effective and beneficial for determining the optima solution for inventory lot sizing problems.