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SynthCharge: An Electric Vehicle Routing Instance Generator with Feasibility Screening to Enable Learning-Based Optimization and Benchmarking

arXiv – CS AI|Mertcan Daysalilar, Fuat Uyguroglu, Gabriel Nicolosi, Adam Meyers|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce SynthCharge, a parametric generator for creating diverse electric vehicle routing problem instances with feasibility screening. The tool addresses limitations in existing benchmark datasets by producing scalable, verifiable instances to enable better evaluation of learning-based routing optimization models.

Key Takeaways

→SynthCharge generates electric vehicle routing problem instances with time windows up to 500 customers, focusing on 5-100 customer experiments.
→The generator includes feasibility screening to filter out unsolvable instances, ensuring structural validity of benchmark datasets.
→Unlike static benchmarks, SynthCharge integrates adaptive energy capacity scaling and range-aware charging station placement.
→The tool aims to provide dynamic benchmarking infrastructure for evaluating neural routing and data-driven approaches.
→Current benchmark datasets lack verifiable feasibility, restricting reproducible evaluation of learning-based routing models.

#electric-vehicles #routing-optimization #machine-learning #benchmarking #research-tools #neural-networks #logistics #optimization

Read Original →via arXiv – CS AI

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