模糊随机环境下考虑信用支付的库存模型与求解算法

Research on Inventory Problem under Trade Credit in Fuzzy Random Environment

笔者按照3种决策准则对含有信用支付的模糊随机库存问题进行了分析,并设计了基于模糊随机模拟、神经网络和遗传算法的混合智能算法,通过3个算例进行说明和验证。结果表明:(1)3种决策准则下构建的库存管理模型能够为决策者提供科学可行的库存管理方法;(2)混合智能算法能够有效求解含有不确定函数的数学规划;(3)信用期限的增加会带来补货周期的增加和供应链总成本的降低。

According to three decision - making criterias, the inventory problem under trade credit in fuzzy random environment is analyzed in this paper, then a hybrid intelligent algorithm by integrating fuzzy random simulation, neural network and genetic algorithm is designed to solve these fuzzy random models. At last, the validity of these models and algorithm is proved by three examples. The empirical research shows： （1） These inventory models can offer decision makers scientific and feasible methods; （2） The proposed hybrid intelligent algorithm can solve uncertain programming models effectively; （3） The computational results show that longer trade credit, in general, may increase the replenishment period and reduce the supply chain＇ s total cost.