A decision support system for data-driven driver-experience augmented vehicle routing problem

Qitong Zhao, Chenhao Zhou, Giulia Pedrielli

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Logistics delivery companies typically deal with delivery problems that are strictly constrained by time while ensuring optimality of the solution to remain competitive. Often, the companies depend on intuition and experience of the planners and couriers in their daily operations. Therefore, despite the variability-characterizing daily deliveries, the number of vehicles used every day are relatively constant. This motivates us towards reducing the operational variable costs by proposing an efficient heuristic that improves on the clustering and routing phases. In this paper, a decision support system (DSS) and the corresponding clustering and routing methodology are presented, incorporating the driver's experience, the company's historical data and Google map's data. The proposed heuristic performs as well as k-means algorithm while having other notable advantages. The superiority of the proposed approach has been illustrated through numerical examples.

Original languageEnglish (US)
Article number500189
JournalAsia-Pacific Journal of Operational Research
Volume37
Issue number5
DOIs
StatePublished - Oct 1 2020

Keywords

  • Clustering
  • Decision support system
  • Heuristics
  • Routing
  • Vehicle routing problem

ASJC Scopus subject areas

  • Management Science and Operations Research

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