A recent study addressed the growing need for efficient delivery services in the expanding e-commerce sector, focusing on real-life consumption data. It proposed a comprehensive modeling framework to evaluate the efficiency of various transportation modes, including Light Commercial Vehicles (LCVs), cargo bikes, and Autonomous Delivery Robots (ADRs). Drones were not included in this study.
Utilizing the Google API, delivery destinations are identified, origin-destination matrices are created, and routes are optimized using Google OR-Tools and a capacitated vehicle routing problem solver. The study’s robustness is further enhanced by incorporating real-life consumption data, considering diverse European contexts, varying urban scales, traffic patterns, and topographical factors, thus assessing their impact on parcel transportation efficiency. The study looked at economic impact, environmental impact, and policy implications.
The findings reveal that ADRs are efficient in pedestrian-focused, traffic-limited areas, while bicycles are more effective in dense city centers. This research highlights the necessity of tailoring transportation mode choices to specific urban characteristics for optimal efficiency and consumer satisfaction. Unfortunately, the research did not model the economic impact of microhubs for cargo bikes and ADRs. The study focused on route optimization.
The present study offers valuable insights into optimizing delivery services in different urban settings, providing a significant model for improving last-mile delivery systems. It contributes to understanding how different transportation modes can be effectively integrated into urban logistics, addressing environmental sustainability, operational efficiency, and real-life consumer demands.
Walther Ploos van Amstel.