Performance optimisations for urban last-mile distribution using autonomous vehicle fleets
File(s)
Author(s)
Liu, Yubin
Type
Thesis
Abstract
Many logistics operators are considering deploying autonomous delivery fleets, such as Mobile Parcel Lockers (MPLs) and Autonomous Delivery Robots (ADRs), to address increasing delivery demands and sustainability challenges in cities. These solutions offer economical and efficient responses to urban delivery bottlenecks, such as time spent on parking and manual delivery modes. Regulatory issues, including requirements for using autonomous vehicles on roads and ensuring pedestrian safety with sidewalk ADRs, are major concerns due to varying laws across regions. Additionally, the feasibility, effectiveness, and capacity limits of deploying autonomous delivery modes in traffic-dense cities need validation before large-scale implementation. Considering the envisioned scale of autonomous delivery fleet deployment, conducting feasibility and capacity assessments becomes imperative.
This study developed a macroscopic assessment model incorporating cost, delivery efficiency,
and pollution emissions to predict the performance of a collaborative delivery pattern integrating human-van delivery, MPL delivery, and ADR delivery in different London boroughs. A congestion game model was proposed to explore customer selection behaviour between MPL and ADR services, providing parameters for future deployments. Mixed-integer programming models for MPL and ADR route planning were established, using reinforcement learning methods and graph theory to ensure solution quality and computational efficiency. Geographic datasets from Illinois, US, were used for the ADR route planning study.
Results indicate that MPL and ADR deployment in urban areas reduces operational costs and improves delivery efficiency compared to conventional manual delivery modes. Their lightweight and easy transportability can alleviate urban traffic congestion and parking space occupancy. Rational curb space planning and parking management strategies can facilitate the integration of autonomous delivery vehicles, promoting wider implementation. For logistics operators, differentiated pricing strategies and capacity deployment based on customer preferences, along with vehicle route planning according to customer time windows and activity trajectories, will reduce operational costs and enhance user convenience.
This study developed a macroscopic assessment model incorporating cost, delivery efficiency,
and pollution emissions to predict the performance of a collaborative delivery pattern integrating human-van delivery, MPL delivery, and ADR delivery in different London boroughs. A congestion game model was proposed to explore customer selection behaviour between MPL and ADR services, providing parameters for future deployments. Mixed-integer programming models for MPL and ADR route planning were established, using reinforcement learning methods and graph theory to ensure solution quality and computational efficiency. Geographic datasets from Illinois, US, were used for the ADR route planning study.
Results indicate that MPL and ADR deployment in urban areas reduces operational costs and improves delivery efficiency compared to conventional manual delivery modes. Their lightweight and easy transportability can alleviate urban traffic congestion and parking space occupancy. Rational curb space planning and parking management strategies can facilitate the integration of autonomous delivery vehicles, promoting wider implementation. For logistics operators, differentiated pricing strategies and capacity deployment based on customer preferences, along with vehicle route planning according to customer time windows and activity trajectories, will reduce operational costs and enhance user convenience.
Version
Open Access
Date Issued
2024-01-29
Date Awarded
01/07/2024
License URL
Advisor
Angeloudis, Panagiotis
Escribano, Jose
Publisher Department
Civil and Environmental Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)