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讨论随机需求环境下的多目标库存控制问题,构建以成本、缺货率和缺货量3个指标最小化为准则的多目标(Q,r)库存模型.为了求出决策者需要的Pareto前沿,设计了基于遗传和差分进化算法的混合智能算法来产生非支配解,进而利用基于熵权的TOPSIS方法对最优解进行排序,此结果可为管理者提供有益的决策参考.
The problem of multi-objective inventory control under stochastic demand environment is discussed, and a multi-objective (Q, r) inventory model is constructed based on the minimization of cost, stockout rate and out-of-stock quantity.In order to obtain the Pareto frontier , A hybrid intelligent algorithm based on genetic and differential evolution algorithms is designed to generate non-dominated solutions. Then TOPSIS based on entropy weight is used to rank the optimal solutions. This result can provide managers with useful decision-making references.