供应链库存的模糊机会约束规划模型

Fuzzy chance constrained programming model for supply chain inventory

研究了不确定环境下的供应链库存优化问题。考虑需求为模糊量,且可能在一定条件下不满足约束条件的决策前提,用三角模糊数表示需求,结合可能性理论中的可信性测度,建立了多品种联合补充的模糊机会约束规划模型,目标函数为最小化供应链订货成本和库存成本的期望值。用遗传算法对优化模型求解,以目标函数值作为染色体适应度,给出了编码方案及选择、交叉、变异算子。用数值实例进行了仿真计算,证明了模型和算法的有效性和性能,并给出了不同置信水平下的计算结果。

Supply chain inventory optimization problem under uncertain environment is concerned. Fuzzy chance con-strained programming model for multi-item joint replenishment is thus proposed, which can take into account fuzzy demand quantity, as well as the constrained conditions are not satisfied to a certain degree. Demand quantity is a triangular fuzzy number, combined with the possibility of credibility measure theory. The objective function is to minimize the expected discounted cost of ordering and inventories in the supply chain. Genetic Algorithm（GA）is used to solve the obtained opti-mality conditions equations, and the fitness function value of the chromosome is the objective value of fuzzy chance con-strained programming model. Chromosome coding, selection, crossover and mutation operations are also studied. The fea-sibility of the model and the effectiveness of the algorithm are illustrated by simulation numerical examples. Some results under different probability level are presented and discussed.