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Vehicle routing problem and driver behaviour: a review and framework for analysis

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  • S. Srivatsa Srinivas
  • M. S. Gajanand

Abstract

Vehicle routing problems (VRPs) whose typical objective is to minimise total travel costs over a tour have evolved over the years with objectives ranging from minimising travel times and distances to minimising pollution and fuel consumption. However, driver behaviour continues to be neglected while planning for vehicle routes. Factors such as traffic congestion levels, monotonous drives and fatigue have an impact on the behaviour of drivers, which in turn might affect their speed-choice and route-choice behaviours. The behaviour of drivers and their subsequent decision-making owing to these factors impact the revenue of transport companies and could lead to huge losses in extreme cases. There have been studies on the behaviour of drivers in isolation, without inclusion of the objectives and constraints of the traditional routing problem. This paper presents a review of existing models of VRP, planner behaviour models in the VRP context and driver behaviour models and provides a motivation to integrate these models in a stochastic traffic environment to produce practical, economic and driver-friendly logistics solutions. The paper provides valuable insights on the relevance of behavioural issues in logistics and highlights the modelling implications of incorporating planner and driver behaviour in the framework of routing problems.

Suggested Citation

  • S. Srivatsa Srinivas & M. S. Gajanand, 2017. "Vehicle routing problem and driver behaviour: a review and framework for analysis," Transport Reviews, Taylor & Francis Journals, vol. 37(5), pages 590-611, September.
  • Handle: RePEc:taf:transr:v:37:y:2017:i:5:p:590-611
    DOI: 10.1080/01441647.2016.1273276
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    Cited by:

    1. Dieter, Peter & Caron, Matthew & Schryen, Guido, 2023. "Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework," European Journal of Operational Research, Elsevier, vol. 311(1), pages 283-300.
    2. An, Heungjo, 2019. "Optimal daily scheduling of mobile machines to transport cellulosic biomass from satellite storage locations to a bioenergy plant," Applied Energy, Elsevier, vol. 236(C), pages 231-243.
    3. Zhilan Lou & Wanchen Jie & Shuzhu Zhang, 2020. "Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations," Sustainability, MDPI, vol. 12(19), pages 1-17, September.
    4. Myroslav OLISKEVYCH & Stepan KOVALYSHYN & Myron MAGATS & Viktor SHEVCHUK & Oleh SUKACH, 2020. "The Optimization Of Trucks Fleet Schedule In View Of Their Interaction And Restrictions Of The European Agreement Of Work Of Crews," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 15(2), pages 157-170, June.
    5. Quirion-Blais, Olivier & Chen, Lu, 2021. "A case-based reasoning approach to solve the vehicle routing problem with time windows and drivers’ experience," Omega, Elsevier, vol. 102(C).

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