文献类型：期刊文献

英文题名：Cold Chain Logistics Distribution Optimization for Fresh Processing Factory Based on Linear Programming Model

作者：Sun, Xue[1,2]; Wu, Chao-Chin[3]; Chen, Liang-Rui[2]

第一作者：Sun, Xue;孙雪

通讯作者：Wu, Chao-Chin

机构：[1] College of Urban Rail Transit and Logistics, Beijing Union University, Beijing, China; [2] Department of Electrical Engineering, National Changhua University of Education, Changhua, Taiwan; [3] Department of Computer Science and Information Engineering, National Changhua University of Education, Changhua, Taiwan

第一机构：北京联合大学城市轨道交通与物流学院

年份：2018

起止页码：593-597

外文期刊名：Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018

收录：EI(收录号：20190406426709)

语种：英文

外文关键词：Warehouses - Linear programming - Supply chains - Transportation routes

摘要：With the development of the economy, the demands for food quality have gradually increased. Cold chain as a temperature-controlled supply chain has emerged to ensure the fresh goods. In recent years, the establishment and improvement of the cold chain logistics, as complex system engineering, is very important. While the cold chain logistics system has been vigorously developing, the common problems we face include: selection of suitable location for cold chain distribution center as well as optimization of distribution route. In this paper, we mainly focus on how to optimize the cold chain logistics distribution for a fresh processing factory M. We propose the methods to optimize the cold chain logistics distribution for M based on linear programming model. First, we adopt location set covering problem (LSCP) to model the location problem of cold chain logistics distribution center for Factory M, and use zero-one programming to solve the distribution center location. Then, we use transshipment problem, which is one of the transportation problem, to model and solve the distribution route for M. Experiments are conducted on IBM ILOG CPLEX Optimization platform, and the suitable plan of logistics distribution for Factory M is figured out at the end. The experiment result shows that the optimal solution using our optimization method can save about 15% of transportation cost compared to the result without any planning. ? 2018 IEEE.