CARGO: A Co-Optimization Framework for EV Charging and Routing in Goods Delivery Logistics

TL;DR

This paper introduces CARGO, a framework that optimizes electric vehicle routing and charging in urban delivery logistics, improving cost efficiency while maintaining delivery time windows.

Contribution

CARGO jointly optimizes EV routing and charging decisions using MILP and heuristics, addressing the NP-hardness of the problem with real-world evaluation.

Findings

Heuristic achieves up to 39% cost reduction compared to baseline strategies.

The problem is proven NP-hard, justifying the need for heuristics.

CARGO's methods perform well on real-world datasets.

Abstract

With growing interest in sustainable logistics, electric vehicle (EV)-based deliveries offer a promising alternative for urban distribution. However, EVs face challenges due to their limited battery capacity, requiring careful planning for recharging. This depends on factors such as the charging point (CP) availability, cost, proximity, and vehicles' state of charge (SoC). We propose CARGO, a framework addressing the EV-based delivery route planning problem (EDRP), which jointly optimizes route planning and charging for deliveries within time windows. After proving the problem's NP-hardness, we propose a mixed integer linear programming (MILP)-based exact solution and a computationally efficient heuristic method. Using real-world datasets, we evaluate our methods by comparing the heuristic to the MILP solution, and benchmarking it against baseline strategies, Earliest Deadline First (EDF) and Nearest Delivery First (NDF). The results show up to 39% and 22% reductions in the charging cost over EDF and NDF, respectively, while completing comparable deliveries.