應用多目標微粒群演算法求解（s, Q）存貨控制最佳化模式

Abstract

The goal of inventory management is how to maintain high level of service quality by using least cost and how to reduce the possibility of shortage in order to satisfy the requirements of customers at the meantime. So inventory management could be regard as a multi-objective optimization problem (MOOP). This work extends Agrell's (1995) inventory control problem from backorder to lost sales, and applies hybrid multi-objective particle swarm optimization (HMOPSO), which incorporates a local search and clustering method, to an inventory planning problem. Next, in order to avoid the redundancies in objective functions, we reorient Agrell's model to two multi-objective inventory control models emerge redundant objective, base on Agrell's objective, we construct two bi-objective inventory models, named the stockout occasions model (N-model) and the number of items stocked out model (B-model). Finally, backorder model is compared to lost sales model. On the views of decision variables, the average safety factor in lost sales model is grater than those in backorder model, but lot size is smaller than backorder model.