Multi-period multi-decision closed-loop logistics network for fresh products with fuzzy variables

doi:10.11772/j.issn.1001-9081.2018122434

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (7): 2168-2174.DOI: 10.11772/j.issn.1001-9081.2018122434

• Frontier & interdisciplinary applications • Previous Articles Next Articles

Multi-period multi-decision closed-loop logistics network for fresh products with fuzzy variables

YANG Xiaohua, GUO Jianquan

Business School, University of Shanghai for Science and Technology, Shanghai 200093, China

Received:2018-12-10 Revised:2019-03-21 Online:2019-07-10 Published:2019-04-08
Supported by:
This work is partially supported by the National Natural Science Foundation of China (71071093, 71471110), the Natural Science Foundation of Shanghai (10ZR1413300), the Innovation Project of Shanghai Science and Technology Commission (16DZ1201402, 16040501500), the Social Science Foundation of Shaanxi Province (2015D060).

模糊环境下多周期多决策生鲜闭环物流网络

杨晓华, 郭健全

上海理工大学管理学院, 上海 200093

通讯作者: 杨晓华
作者简介:杨晓华(1995-),女,浙江温州人,硕士研究生,主要研究方向:闭环物流网络;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（71071093，71471110）；上海市自然科学基金资助项目（10ZR1413300）；上海市科委创新项目（16DZ1201402，16040501500）；陕西省社会科学基金资助项目（2015D060）。

Abstract

Abstract:

Concerning the high frequency logistics distribution of fresh products due to the products' perishability and vulnerability, as well as the uncertainty of demand and return, a multi-period closed-loop logistics network for fresh products with fuzzy variables was constructed to achieve the multi-decision arrangement of minimum system cost, optimal facility location and optimal delivery route. In order to solve the Fuzzy Mixed Integer Linear Programming (FMILP) model corresponding to the system, firstly, the amounts of demand and return were defined as triangular fuzzy parameters; secondly, the fuzzy constraints were transformed into crisp formula by using fuzzy chance constrained programming method; finally, the optimal solution of case was obtained by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. The experimental results show that multi-period closed-loop system performs better than single-period system in the aspect of multi-decision programming, meanwhile, the confidence levels of triangular fuzzy parameters have significant influence on the optimal operation of enterprise, thus providing a reference for relevant decision makers.

Key words: fresh product, multi-period delivery, closed-loop logistics network, fuzzy chance constrained programming, mixed integer linear programming, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm

摘要：

针对生鲜品因易腐易损性而产生的高频次物流配送及不确定需求与退货量的问题，提出了模糊环境下多周期生鲜闭环物流网络系统，以实现最小系统成本、最优设施选址与最佳配送路径的多决策安排。为求解系统对应的模糊混合整数线性规划（FMILP）模型，首先将生鲜需求量和退货量设定为三角模糊值，其次运用模糊机会约束规划方法将模糊约束等价变换为清晰式，最后利用遗传算法（GA）和粒子群优化（PSO）算法搜索案例的最优解。实验结果表明，多周期闭环系统比单周期更能兼顾多决策规划，同时三角模糊量的置信水平变化对企业最优运作有着显著影响，进而为相关决策者提供借鉴。

关键词: 生鲜, 多周期配送, 闭环物流网络, 模糊机会约束规划, 混合整数线性规划, 遗传算法, 粒子群优化算法

CLC Number:

F272
TP18

YANG Xiaohua, GUO Jianquan. Multi-period multi-decision closed-loop logistics network for fresh products with fuzzy variables[J]. Journal of Computer Applications, 2019, 39(7): 2168-2174.

杨晓华, 郭健全. 模糊环境下多周期多决策生鲜闭环物流网络[J]. 计算机应用, 2019, 39(7): 2168-2174.

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Multi-period multi-decision closed-loop logistics network for fresh products with fuzzy variables

模糊环境下多周期多决策生鲜闭环物流网络

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