Comparative study of lot-sizing methods in distribution requirements planning

This research deals with lot-sizing problems in which the fuzzy set theory is used to model two of the most important properties of inventory management in logistics enterprises: the uncertain demand and quality utilization of perishable goods (e.g., vegetables, fruits, meat, dairy products, etc.). Seven commonly used and well-known lot-sizing methods are selected from the literature and are modified to incorporate demand uncertainty and quality utilization through fuzzy modeling. The seven models are compared using simulation under distribution requirements planning (DRP) environment that resembles the multi-echelon distribution structure in logistics enterprises. Several key parameters of the DRP system are taken into account in the simulation study, which include demand patterns, cost coefficients and utilization functions. The performance measures of this study are total operating cost and quality utility upon demand. For validation and application purposes, actual data provided by a logistics company is used to drive the simulation, and the results are presented. Intensive computational results show that the performances of the seven models are significantly different under a variety of simulation environments. Hence, the optimal inventory policy is suggested in accordance with different system environments and performance measures.