基于模糊随机需求的B2C多品采配协同模型及其粒子群算法求解  

A particle swarm algorithm for a novel B2C multi-item replenishment and delivery coordination model with fuzzy random demands

作　　者：崔利刚 任海利 邓洁 张亚军[3] CUI Ligang;REN Haili;DENG Jie;ZHANG Yajun(School of Economics and Management,Chongqing Jiaotong University,Chongqing 400074,China;Intellectual Property Institution of Chongqing,Chongqing University of Technology,Chongqing,400050,China;School of Business Administration,Guizhou University of Finance and Economics,Guiyang 550025,China)

机构地区：[1]重庆交通大学经济与管理学院,重庆400074 [2]重庆理工大学知识产权学院,重庆400050 [3]贵州财经大学工商管理学院,贵阳550025

出　　处：《管理工程学报》2020年第6期183-190,共8页Journal of Industrial Engineering and Engineering Management

基　　金：国家自然科学基金资助项目(71602015);教育部人文社科青年基金资助项目(16YJC630014);重庆市基础与前沿研究计划项目(cstc2016jcyjA0530);贵州省教育厅课题(黔教合KY字[2018]159);重庆市研究生科研创新项目(CYS17216)。

摘　　要：面对繁多的商品品类和多变的采配流程,B2C(Business to Consumers)电商物流运营的主要挑战之一即是合理刻画顾客小批量、不确定性的需求以实现采配流程的平稳高效运行。本文针对B2C电商企业多品补货需求的不确定性,将商品需求假设为三角模糊变量。同时,考虑基于历史数据的模糊变量确定存在随机可能性判断,本文运用模糊期望值理论,构建以成本最小化为目标的多品采配(Joint replenishment and delivery,JRD)协同模型。模型采用粒子群算法(Particle Swarm Optimization,PSO)进行求解。数值实验验证了PSO相比于遗传算法(Genetic Algorithm,GA)求解JRD的有效性和适用性。The rapid development and extraordinary achievements of e-commerce in China in recent years have attracted worldwide attention. As more and more companies embrace e-commerce, the fierce competition between e-commerce companies and traditional companies or even e-commerce companies erupted in an unprecedented situation. In all e-commerce models, business-to-consumer(B2 C) is characterized by providing high-quality goods and services, reflecting greater development potential and stronger competitive strength than its rivals. However, in the face of a wide range of commodity categories and various operations of replenishment-delivery processes, B2 C e-commerce logistics operations are under tremendous pressure to solve the problem of how to reasonably characterize the needs of customers in small quantities and uncertain demands to achieve stable and efficient operation of replenishment-delivery processes. By assuming the fuzzy demands of customers, this paper studies the multi-product joint replenishment and delivery(JRD) coordination problem of B2 C e-commerce enterprises. First of all, in order to better assess and predict customer demand for uncertainty, we define commodity demand as a triangular fuzzy variable based on subjective judgments of different experts. Secondly, considering that the fuzzy interval is obtained based on empirical knowledge data and the fuzzy variables may be judged by different experts when using fuzzy variables, this paper uses fuzzy expected value theory to construct a fuzzy random JRD model to minimize the total cost of the system change. Thirdly, considering that the JRD model is essentially an NP-hard problem, this paper designs a particle swarm optimization(PSO) algorithm to solve the model. For the PSO algorithm, this paper designs a real and integer mixed encoding scheme to represent the basic processing cycle, the replenishment-delivery frequencies of multi-items. Fourthly, through the numerical experiment for the parameter sensitivity analysis, the optimal parameter combination

关 键 词：企业对消费者 联合补货及配送 模糊需求 粒子群算法 

分 类 号：F252.3[经济管理—国民经济] F272.3