基于改进遗传算法的生鲜多目标闭环物流网络模型

doi:10.11772/j.issn.1001-9081.2019091682

计算机应用 ›› 2020, Vol. 40 ›› Issue (5): 1494-1500.DOI: 10.11772/j.issn.1001-9081.2019091682

• 应用前沿、交叉与综合 • 上一篇下一篇

基于改进遗传算法的生鲜多目标闭环物流网络模型

霍晴晴¹, 郭健全²

1.上海理工大学管理学院，上海 200093
2.上海理工大学上海-汉堡国际工程学院，上海 200093

收稿日期:2019-10-08 修回日期:2019-12-06 出版日期:2020-05-10 发布日期:2020-05-15
通讯作者: 霍晴晴（1996—）
作者简介:霍晴晴（1996—），女，江苏徐州人，硕士研究生，主要研究方向：物流工程；郭健全（1972—），男，河南鲁山人，副教授，博士，主要研究方向：国际贸易、国际商务、国际物流、供应链管理。
基金资助:
国家自然科学基金资助项目（71471110）；上海市科技创新行动计划项目（16DZ1201402）；上海市科委院校能力建设项目（16040501500）。

Multi-objective closed-loop logistics network model of fresh foods based on improved genetic algorithm

HUO Qingqing¹, GUO Jianquan²

1.Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
2.Shanghai-Hamburg College, University of Shanghai for Science and Technology, Shanghai 200093, China

Received:2019-10-08 Revised:2019-12-06 Online:2020-05-10 Published:2020-05-15
Contact: HUO Qingqing, born in 1996, M.S. candidate. Her research interests include logistics engineering.
About author:HUO Qingqing, born in 1996, M.S. candidate. Her research interests include logistics engineering.GUO Jianquan, born in 1972, Ph. D, associate professor. His research interests include international trade, international commerce, international logistics, supply chain management.
Supported by:
This work is partially supported by the National Natural Science Foundation of China (71471110),the Shanghai Science and Technology Innovation Action Plan Project (16DZ1201402), the College Capacity Building Project of Shanghai Municipal Science and Technology Commission (16040501500).

摘要/Abstract

摘要：

针对生鲜产品闭环物流网络中存在的经济成本高、碳排放量大、社会效益重视不足等问题，综合考虑退货量的不确定性，以经济成本最小、碳排放最小、社会效益最大为目标，建立了不确定条件下的生鲜多目标闭环物流网络模型。首先，利用改进的遗传算法（GA）求解该模型；然后，结合上海某生鲜企业运营管理数据，验证了模型的可行性；最后，将改进的GA的结果与粒子群优化（PSO）算法的结果对比，验证了算法的有效性，凸显了改进的GA在求解多目标的复杂约束问题时的优越性。算例结果表明，多目标优化满意度达到0.92，高于单目标优化满意度，展示了所提模型的有效性。

关键词: 生鲜闭环物流网络, 不确定条件, 多目标模型, 遗传算法, 粒子群优化算法

Abstract:

In order to solve the problems of high economic costs, large amount of carbon emissions and insufficient attention to social benefits in the closed-loop logistics network for fresh foods, a multi-objective closed-loop logistics network model for fresh foods under uncertain conditions was established by considering the uncertainty of return quantity and aiming at the minimum economic costs, the minimum carbon emissions and the maximum social benefits. Firstly, the improved Genetic Algorithm (GA) was used to solve the model. Then, the feasibility of the model was verified by combining the operation and management data of a fresh food enterprise in Shanghai. Finally, the results of improved GA was compared to the results of Particle Swarm Optimization (PSO) algorithm to verify the effectiveness of the algorithm， and to highlight the superiority of the improved GA in solving multi-objective complex constraint problems. The example results show that the satisfaction degree of multi-objective optimization is 0.92, which is higher than that of single-objective optimization, demonstrating the effectiveness of the proposed model.

Key words: fresh food closed-loop logistics network, uncertain condition, multi-objective model, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm

中图分类号:

TP18

霍晴晴, 郭健全. 基于改进遗传算法的生鲜多目标闭环物流网络模型[J]. 计算机应用, 2020, 40(5): 1494-1500.

HUO Qingqing, GUO Jianquan. Multi-objective closed-loop logistics network model of fresh foods based on improved genetic algorithm[J]. Journal of Computer Applications, 2020, 40(5): 1494-1500.

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基于改进遗传算法的生鲜多目标闭环物流网络模型

Multi-objective closed-loop logistics network model of fresh foods based on improved genetic algorithm

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