逆向物流供应链最佳供应商选择及订单量分配*

为解决逆向物流供应链中,供应商选择、订单量分配和提货点位置等不确定问题,建立了一个新的模糊多目标数学模型来确定最佳供应商选择、供应量及提货点位置,为避免在解决多目标模型时人为主观赋权,运用基于模糊目标规划的蒙特卡罗仿真模型来求解帕累托(pareto)理想解,采用遗传算法进行求解,并给出了相应优化方案,在此基础上研究讨论了不同权重分配下结果的优劣性及供应商选择风险,最后,针对不同权重分配,比较了遗传算法和Gurobi求解,实验表明,对于该问题模型遗传算法在解的优劣性上优于Gurobi。

Best supplier selection and order quantity allocation in reverse logistics supply chain

in this research of reverse logistics supply chain , in order to solve the problem of uncertainty of supplier selection and order quantity allocation in reverse logistics supply chain, a new fuzzy multi-objective mathematical model was established to determine the supplier selection and the optimal supplier supply and delivery point location. In order to avoid the subjective weighting from decision makers when solving the multi-objective model, a Monte Carlo simulation integrated with fuzzy goal programming is developed to determine the entire set of pareto-optimal solutions of the proposed model . GA is used to solve this model and the paper get a scheduling optimization plan, analysis and study of the multi assigning weights in the objective function the pros and cons and supplier selection risk. Finally, the GA and Gurobi algorithm are compared for different weight distribution. The experimental results show that the GA is superior to Gurobi in solving the problem