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

2025-08-02

AI Summary

This work addresses the joint optimization of routing and charging strategies for electric delivery trucks under time-window constraints in urban logistics. We formulate a precise mixed-integer linear programming (MILP) model that simultaneously incorporates multi-dimensional charging constraints—including charger availability, charging cost, geographic distance, and real-time state-of-charge—while ensuring feasible route execution. To enhance computational scalability, we propose an efficient heuristic algorithm tailored to this integrated problem. Extensive experiments on real-world logistics data demonstrate that our approach reduces charging costs by 39% and 22% compared to the Earliest-Deadline-First (EDF) and Nearest-Distance-First (NDF) baselines, respectively, while maintaining equivalent task completion rates. To the best of our knowledge, this is the first framework to jointly optimize routing and charging under comprehensive, realistic charging infrastructure constraints. The results significantly improve both operational efficiency and economic viability of electric urban freight systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Optimizing EV charging and routing for goods delivery logistics
• Addressing limited battery capacity and charging point constraints
• Reducing charging costs while meeting delivery time windows

Innovation

• Joint optimization of EV routing and charging
• MILP-based exact and heuristic solutions
• Real-world dataset validation with cost savings