Vehicle routing problem (VRP) research has recently improved dramatically to simulate more real-life circumstances. Nevertheless, the typical VRP models proposed have been isolated from the most important factor determining the success of the VRP plan on the ground, i.e. the human factor (driver). Thus, this research investigates the effect of drivers’ behaviours on the optimal VRP plan by integrating the level of autonomy of both planner and drivers as represented by risk-taking parameters. To enhance the model configuration’s practicability, the concept of ‘ridesharing’ e which has been introduced before e has also been integrated to expand the logistical services, improve customer satisfaction, and compensate for shortages in service. Moreover, to ensure environmentally-friendly logistical practices, a velocity maximisation policy and environmental penalty enforcement on the chosen velocity range have been considered. In general, the model improves drivers’ satisfaction, customers’ perceived quality, and the firm’s financial objectives. Additionally, it achieves a better supply chain strategic fit by planning at the three levels: strategic, tactical, and operational. A numerical example was solved using the Eclipse Java 2018-09 solver through two heuristic methods, the Greedy and the Intra-route neighborhood heuristic, and both revealed the same near-optimal solutions. Sensitivity analyses showed that the resulting insignificant increase in the VRP costs due to assigning autonomy for drivers is still reasonable, and the total costs’ objective function weight has an insignificant effect on the total near-optimal solution, while that of the energy consumption objective function has the largest impact.

Introduction

One of the most popular applications of operation research science is the VRP, which was defined by Clarke and Wright (1964) as serving customer networks distributed at different geographic points, using fleets of trucks from different capacities. VRP-related studies have exponentially grown by around 6% per year in the research literature (Eksioglu et al., 2009). Furthermore, VRP has grasped its importance due to its wide usage in logistic and transportation aspects. Moreover, the literature has concentrated on different variants related to VRP, in which scholars set models and solutions for many real-life problems to control difficulties associated with different stages of transportation, such as travel times, pick-up and delivery time windows, and information input (Braekers et al., 2016). Paraskevopoulos et al. (2017) stated that the routing and scheduling planning processes confront some challenges in allocating scarce resources for certain services. On the other hand, the VRP topic has been widely analysed due to its impact on various industries (Lahyani et al., 2017). Based on a taxonomic review related to the VRP topic during the period 2009e2015, it was revealed that the literature focused on some important VRP aspects, and different models were suggested to solve the problems of different objectives, including the capacitated VRP, periodic VRPs, the VRP with time windows, and others (Braekers et al., 2016). Additionally, a very crucial aspect that considerably affects the near-optimal results required for such a VRP case is augmenting the human factor in VRP models.