A Decision Support System for Data-Driven Driver-Experience Augmented Vehicle Routing Problem

Qitong Zhao, Chenhao Zhou () and Giulia Pedrielli ()
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Qitong Zhao: School of Business, Singapore University of Social Sciences, 463 Clementi Rd, Singapore University of Social Sciences, Singapore 599494, Singapore
Chenhao Zhou: Department of Industrial Systems Engineering and Management, National University of Singapore, 3 Research Link, Innovation 4.0, University of Singapore, Singapore 117602, Singapore
Giulia Pedrielli: School of Computing Informatics and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ 85281, USA

Asia-Pacific Journal of Operational Research (APJOR), 2020, vol. 37, issue 05, 1-23

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.

Keywords: Decision support system; vehicle routing problem; heuristics; clustering; routing (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0217595920500189

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