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SynthCharge generator creates feasible EV routing instances for AI benchmarking

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SynthCharge, a new generator for electric vehicle routing problem instances. This tool aims to address the limitations of existing static datasets by creating diverse and feasible instances for benchmarking learning-based optimization models. SynthCharge incorporates adaptive scaling for battery capacity and strategic charging station placement, and it includes a screening process to filter out unsolvable scenarios, thereby supporting the systematic evaluation of emerging neural routing approaches. AI

IMPACT Enables more robust evaluation of AI-driven routing solutions for electric vehicles.

RANK_REASON The cluster is about a research paper introducing a new instance generator for a specific optimization problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SynthCharge generator creates feasible EV routing instances for AI benchmarking

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24 / 100
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The cluster is about a research paper introducing a new instance generator for a specific optimization problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mertcan Daysalilar, Fuat Uyguroglu, Gabriel Nicolosi, Adam Meyers ·

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

    arXiv:2603.03230v2 Announce Type: replace-cross Abstract: The electric vehicle routing problem with time windows (EVRPTW) extends the classical VRPTW by introducing battery capacity constraints and charging station decisions. Existing benchmark datasets are often static and lack …

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