配送中心
计算机科学
比例(比率)
选择(遗传算法)
过程(计算)
分布(数学)
最优化问题
运筹学
运输工程
数学优化
业务
工程类
地理
数学
人工智能
算法
营销
数学分析
操作系统
地图学
作者
Wenhao Jia,Yang Lin,Junyuan Ding,G. Qin,Shuai Shao,Ye Tian
标识
DOI:10.1007/978-981-97-2275-4_20
摘要
The transportation process of fresh food often experiences spoilage, leading to a negative impact on its freshness and sales. Although the existing logistics distribution centers can transport a large number of goods, they have poor timeliness, so they are not suitable for large-scale fresh food transportation. In order to ensure the freshness and sales of fresh food, it is necessary to establish multi-level logistics distribution centers, hence the location of distribution centers has become a key issue. Such facility location problems are challenging in both modeling and optimization, especially when facing thousands of communities in a city that is ubiquitous in China. In this paper, a large-scale multi-objective optimization model for distribution center location problem is formulated and solved by sparse multi-objective optimization evolutionary algorithms (sparse MOEAs). Experimental results on the formulated optimization model show that the center locations obtained by the state-of-the-art sparse MOEAs can effectively reduce the cost of logistics construction, optimize the loss in the transportation process, and improve the overall benefit.
科研通智能强力驱动
Strongly Powered by AbleSci AI