Some of these solutions are easier to implement than the others. These solutions include, but are not limited to, cargo bikes, self-service parcel lockers, aerial drones, ground based autonomous delivery robots, crowd shipping, and collection-and-delivery points ( Boysen et al., 2020 Janjevic et al., 2019).
Given these factors, many logistics service providers are exploring alternative solutions for last-mile deliveries. With the spread of COVID−19, demand for last-mile delivery services has increased even further. In addition, the rapid growth of e-commerce has increased the demand for delivery drivers ( Sasso, 2018) and resulted in driver shortages. Partially, this is due to the number of failed deliveries, which leads to additional delivery attempts and increased costs. It is estimated that the last mile constitutes about half of all logistics costs for service providers ( McKinsey and Company, 2016). There are many challenges associated with last-mile delivery, the final step in the retail supply chain. In addition, we find that extended time windows may help increase service quality in zones with high pedestrian density by up to 40%. We demonstrate that the presence of pedestrian zones leads to alternative path choices in 30% of all cases. The heuristic solution approach uses the minimum travel time paths from different LOS zones (path flexibility). The model includes an objective that reflects customer service quality based on early and late arrivals. We model this new problem with stochastic travel times and soft customer time windows. Pedestrian LOS is a measure of pedestrian flow density.
In this paper, we investigate a robot-based last-mile delivery problem considering path flexibility given the presence of zones with varying pedestrian level of service (LOS). Since delivery robots share sidewalks with pedestrians, it may be beneficial to choose paths for them that avoid zones with high pedestrian density.
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