Evolutionary multi-objective optimization for multi-depot vehicle routing in logistics

Xiaowen Bi; Zeyu Han; Wallace K. S. Tang

doi:10.2991/ijcis.10.1.94

Author(s)

Related Research Unit(s)

Department of Electrical Engineering

Detail(s)

Original language	English
Pages (from-to)	1337-1344
Journal / Publication	International Journal of Computational Intelligence Systems
Volume	10
Issue number	1
Publication status	Published - 2017

Link(s)

DOI	DOI https://doi.org/10.2991/ijcis.10.1.94 Final Published version
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Attachment(s)	Documents 18741093.pdf Final Published version, 1.42 MB, PDF Publisher's Copyright Statement This full text is made available under CC-BY-NC 4.0. https://creativecommons.org/licenses/by-nc/4.0/
Link to Scopus	https://www.scopus.com/record/display.uri?eid=2-s2.0-85034841358&origin=recordpage
Permanent Link	https://scholars.cityu.edu.hk/en/publications/publication(a594cd8a-6b4c-4cab-8823-ee53a66e18ad).html

Abstract

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.