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

arXiv: https://arxiv.org/abs/2508.01476 · HTML: https://mlsanity.com/paper/2508.01476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 44 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services](https://mlsanity.com/paper/2203.15127/) (2022)
- [Hybrid Memetic Search for Electric Vehicle Routing with Time Windows, Simultaneous Pickup-Delivery, and Partial Recharges](https://mlsanity.com/paper/2410.19580/) (2024)
- [Vehicle-to-Vehicle Charging Coordination over Information Centric Networking](https://mlsanity.com/paper/2206.09310/) (2022)
- [On the Impact of Co-Optimizing Station Locations, Trip Assignment, and Charging Schedules for Electric Buses](https://mlsanity.com/paper/2403.09763/) (2024)
- [SMEVCA: Stable Matching-based EV Charging Assignment in Subscription-Based Models](https://mlsanity.com/paper/2412.09948/) (2024)

## Abstract (arXiv)

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.