Optimal Agent-Based Pickup and Delivery with Time Windows and Electric Vehicles

Abstract

The traditional methods of transporting goods and people in urban areas using vehicles powered by internal combustion engines are major contributors to pollution. As a result, an increasing number of logistics companies are transitioning to electric vehicles (EVs) for daily operations, replacing traditional engines. This shift opens research avenues regarding the integration of EVs into delivery workflows and how this can contribute to greener cities. This study tackles the EV routing problem, focusing on balancing battery constraints and optimizing routes. We formulated the problem as a pickup and delivery with time windows, incorporating electric energy consumption constraints, and utilized consensus mechanisms in an agent-based simulation context. Our evaluation used 15 scenarios, capturing variations in vehicle configurations, order generation rates, and battery and freight capacities. We compared two order allocation strategies: “Closest Allocation” and “Negotiation” consensus-based allocation. The results confirmed that the consensus-based strategy outperformed the “Closest Allocation” in metrics such as remaining orders, orders not handled in time, total distance traveled, total recharging cost, and total number of recharges. These findings have significant implications for urban planners, logistic companies, and policymakers, demonstrating that an agent-based simulation context for electric vehicles using consensus-based strategies can enhance delivery efficiency and promote sustainability.