SynthCharge is a parametric generator that creates diverse, feasibility‑screened instances of the electric vehicle routing problem with time windows (EVRPTW). It produces instances ranging from 5 to 100 customers (up to 500 in theory) with adaptive energy capacity scaling and range‑aware charging station placement, filtering out unsolvable cases via a fast feasibility screening process. This dynamic benchmarking infrastructure enables systematic evaluation of learning‑based routing and data‑driven approaches.
By Mertcan Daysalilar, Fuat Uyguroglu, Gabriel Nicolosi, Adam Meyers
The paper presents a GeoAI framework that uses a variational autoencoder and a graph convolutional network to analyze high‑dimensional geospatial data for electric vehicle charging station location selection. By compressing EV usage, land‑use, population, and traffic attributes into a latent space, the model predicts suitable future station sites and identifies 27 new candidates in Bryan‑College Station, Texas. Two policy scenarios—maximizing geospatial similarity versus minimizing travel distance—demonstrate how different strategic objectives influence station placement outcomes.
By Eun Hak Lee, Euntak Lee
As electric vehicle (EV) adoption increases, ensuring efficient and well-distributed charging infrastructure has become a critical challenge. While many EV charging station location problem (CSLP) stu...
arXiv:2607. 06066v1 Announce Type: new Abstract: The Vehicle Routing Problem (VRP) and its variants represent some of the most practically consequential optimization challenges in modern logistics and urban mobility.
By Manish Kolachalam, Rani Malhotra
arXiv:2510. 21127v2 Announce Type: replace-cross Abstract: Despite rapid advancements in sensor networks, conventional battery-powered sensor networks suffer from limited operational lifespans and frequent maintenance requirements that severely constrain their deployment in remote and inaccessible environments.
By Bowei Tong, Hui Kang, Jiahui Li, Geng Sun, Jiacheng Wang, Yaoqi Yang, Bo Xu, Dusit Niyato
The paper introduces a Large Language Model–enhanced Multi-Agent Reinforcement Learning framework for optimizing electric vehicle charging, station profitability, and grid stability in public charging systems. By using an LLM to select interpretable features from IoT data and dynamically balance conflicting objectives, the approach unifies grid, EV, and station optimization in a single loop. Experiments show the method outperforms existing baselines, improving market efficiency and cutting training time by more than 70%.
By Yang Zhang, Lindong Xie, Chongyu Wang, Gaojunjie Li, Siqi Bu, Edward Chung