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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

4 Scopus Citations
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Original languageEnglish
Pages (from-to)1337-1344
Journal / PublicationInternational Journal of Computational Intelligence Systems
Issue number1
Publication statusPublished - 2017



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.

Research Area(s)

  • Evolutionary algorithm, Local search, Multi-depot vehicle routing, Multi-objective optimization

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