关键词: 随机需求, 随机提前期, 最优库存控制, 实时追踪, 动态响应微粒群算法

Abstract: In order to resolve the optimal inventory control problem with random demands and lead time, a heuristic Dynamic Response Particle Swarm Optimization（DRPSO） is presented. The dynamic nonlinear optimal model of Optimal Inventory Control Policy（OICP） is established in supply chain system, and assuming that customer demand and lead time are linear and Gaussian random variable respectively, based on which, the actual process of economic operators is simulated by setting different variations, increments and frequency. On the basis of standard PSO algorithm, the adaptive mutation probability and the response mode of dynamic updating are introduced to improve the adaptability of particles for the dynamic environment. To simulate the uncertain supply chain environment, two types of goal movement with various uncertain demands and lead-time are investigated, which shows to be effective in locating the changing best order quantity and recorder point.

Key words: random demands, random lead time, optimal inventory control policy, real-time track, dynamic response particle swarm optimization