# Joint Routing and Charging Problem of Multiple Electric Vehicles: A Fast Optimization Algorithm

arXiv: https://arxiv.org/abs/2105.05711 · HTML: https://mlsanity.com/paper/2105.05711/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2021.

## Built on by (most cited first, 2 of 2)

- [LLM4Branch: Large Language Model for Discovering Efficient Branching Policies of Integer Programs](https://mlsanity.com/paper/2605.10401/) (2026)
- [Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAV](https://mlsanity.com/paper/2410.04320/) (2024)

## Abstract (arXiv)

Logistics has gained great attentions with the prosperous development of commerce, which is often seen as the classic optimal vehicle routing problem. Meanwhile, electric vehicle (EV) has been widely used in logistic fleet to curb the emission of green house gases in recent years. Solving the optimization problem of joint routing and charging of multiple EVs is in a urgent need, whose objective function includes charging time, charging cost, EVs travel time, usage fees of EV and revenue from serving customers. This joint problem is formulated as a mixed integer programming (MIP) problem, which, however, is NP-hard due to integer restrictions and bilinear terms from the coupling between routing and charging decisions. The main contribution of this paper lies at proposing an efficient two stage algorithm that can decompose the original MIP problem into two linear programming (LP) problems, by exploiting the exactness of LP relaxation and eliminating the coupled term. This algorithm can achieve a nearoptimal solution in polynomial time. In addition, another variant algorithm is proposed based on the two stage one, to further improve the quality of solution.