Evolutionary Multi-objective Optimization for Multi-depot Vehicle Routing in Logistics

被引：3

作者：

Bi, Xiaowen ^{[1
]}

Han, Zeyu ^{[1
]}

Tang, Wallace K. S. ^{[1
]}

机构：

[1] City Univ Hong Kong, Dept Elect Engn, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China

来源：

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | 2017年 / 10卷 / 01期

关键词：

Multi-depot vehicle routing; multi-objective optimization; evolutionary algorithm; local search; GENETIC ALGORITHM; NSGA-II; SEARCH;

D O I：

10.2991/ijcis.10.1.94

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Delivering goods in an efficient and cost-effective way is always a challenging problem in logistics. In this paper, the multi-depot vehicle routing is focused. To cope with the conflicting requirements, an advanced multi-objective evolutionary algorithm is proposed. Local-search empowered genetic operations and a fuzzy cluster-based initialization process are embedded in the design for performance enhancement. Its outperformance, as compared to existing alternatives, is confirmed by extensive simulations based on numerical datasets and real traffic conditions with various customers' distributions.

引用

页码：1337 / 1344

页数：8

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