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针对(Q,R)策略研究将一定时期内的随机需求期望值默认为最优采购总量,或未计算相邻两个订货时刻之间的成本等不足,同时考虑累进制数量折扣与订货批量约束及库容限制,以采购商相邻两次订货间单位时间期望总成本最小为目标建立(Q,R)优化模型;以均匀分布需求为例给出基于粒子群算法的求解方法;通过仿真分析采购商自身需求和成本参数对其最优(Q,R)策略的影响,讨论固定订货费、订货批量约束及数量折扣契约不同的供应商选择问题.结果表明,资源约束与数量折扣下的(Q,R)优化及供应商选择问题复杂,很难根据直觉进行决策,恰当的累进制数量折扣机制可促进订货批量增大并降低采购商成本.本文将安全库存优化扩展到资源有限与特定契约情形,可为供应商选择与库存控制提供依据.“,”The published literature on (Q,R) policy has acquiesced in that the optimal total purchase quantity is the expected value of the stochastic demand in a certain period or have not taken the cost between two successive orders into account.A (Q,R) optimization model was developed for a buyer faced with stationary stochastic demand,incremental quantity discount and order quantity and its capacity constraints to minimize the total expected cost per unit time between two successive orders.A solution based on particle swarm optimization was proposed for the model when the demand was uniformly distributed.Some simulation results showed the effects of the buyer's demand and cost parameters on its optimal (Q,R) policy and the corresponding minimum cost.The selection of suppliers with different order costs,order quantity boundaries and incremental quantity discounts was also discussed in detail through simulation.The results show that the (Q,R) policy optimization and supplier selection under resource constraints and quantity discount are too complicated to decide on intuition,and an appropriate incremental quantity discount can increase the order quantity and reduce the cost of the buyer.This proposed methodology extends (Q,R) policy optimization to the setting with resource constraints and supply chain contracts and can provide basis for supplier selection and inventory control in the above-mentioned circumstances.