逆向物流供应链最佳供应商选择及订单量分配

发布时间：2018-04-19 05:32

本文选题：逆向物流供应链 + 模糊目标规划　；参考：《计算机应用研究》2017年04期

【摘要】：在逆向物流供应链研究中,为了解决市场需求、供应商供货能力、回收产品数量等不确定问题以及逆向物流系统中存在的目标冲突,建立了制造商收益最大化和所选供应商不合格零件数最少化的多目标数学优化模型来确定最佳供应商选择、订单量分配以及提货点选址。运用基于模糊目标规划的蒙特卡罗仿真模型把多目标函数重构成单目标函数。采用自适应遗传算法(AGA)对单目标函数进行求解,并给出了最佳供应商选择及订单量分配。在此基础上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,比较了自适应遗传算法和Gurobi求解。实验表明,对于该问题模型自适应遗传算法在解的运行速度以及精度上都优于Gurobi。
[Abstract]:In the research of reverse logistics supply chain, in order to solve the uncertain problems such as market demand, supplier supply capacity, quantity of recovered products, and the conflict of objectives in reverse logistics system,A multi-objective mathematical optimization model is established to maximize the profit of the manufacturer and minimize the number of unqualified parts of the selected supplier to determine the optimal supplier selection, order allocation and pick-up location.Monte-Carlo simulation model based on fuzzy objective programming is used to reconstitute multi-objective functions into single-objective functions.Adaptive genetic algorithm (AGA) is used to solve the single objective function, and the optimal supplier selection and order quantity allocation are given.On this basis, the advantages and disadvantages of the results and the risk of supplier selection under different weight distribution are discussed.Finally, the adaptive genetic algorithm and Gurobi solution are compared for different weight allocation.Experiments show that the adaptive genetic algorithm is superior to Gurobim in the speed and accuracy of the solution.
【作者单位】：上海海事大学物流研究中心;
【基金】：国家自然科学基金资助项目(71471110,71301101) 上海市科委重点资助项目(12510501600);上海市科委工程中心能力提升项目(14DZ2280200) 上海海事大学横向科研项目(20140057)
【分类号】：F274;TP18

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