A Decision Support System for Data-Driven Driver-Experience Augmented Vehicle Routing Problem
Qitong Zhao,
Chenhao Zhou () and
Giulia Pedrielli ()
Additional contact information
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
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0217595920500189
Access to full text is restricted to subscribers
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:wsi:apjorx:v:37:y:2020:i:05:n:s0217595920500189
Ordering information: This journal article can be ordered from
DOI: 10.1142/S0217595920500189
Access Statistics for this article
Asia-Pacific Journal of Operational Research (APJOR) is currently edited by Gongyun Zhao
More articles in Asia-Pacific Journal of Operational Research (APJOR) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().