生成可验证可行性的电动车路径规划数据集，支持学习型优化模型评估
SynthCharge: An Electric Vehicle Routing Instance Generator with Feasibility Screening to Enable Learning-Based Optimization and Benchmarking
- 基于参数化生成器动态构造多样路径实例
- 通过快速可行性筛选剔除不可解案例，保证数据质量
- 适合研究神经路径优化与数据驱动算法的学者使用
电动车辆路径问题带时间窗（EVRPTW）在经典VRPTW基础上引入了电池容量限制和充电站决策。现有基准数据集通常静态且缺乏可验证的可行性，制约了学习型路径模型的可复现评估。本文提出SynthCharge，一个参数化生成器，可在不同时空配置和可扩展客户数量下生成多样化、经可行性筛选的EVRPTW实例。尽管目前可生成最多500个客户的大型实例，实验聚焦于5至100个客户规模。与静态基准套件不同，SynthCharge将实例几何结构与自适应能量容量缩放及范围感知的充电站布局相结合。为确保结构有效性，生成器通过快速可行性筛查过程系统剔除不可解实例。最终，SynthCharge提供了动态基准测试基础设施，可用于系统评估新兴神经路径与数据驱动方法的鲁棒性。
原文摘要 · Abstract (English)
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 verifiable feasibility, which restricts reproducible evaluation of learning-based routing models. We introduce SynthCharge, a parametric generator that produces diverse, feasibility-screened EVRPTW instances across varying spatiotemporal configurations and scalable customer counts. While SynthCharge can currently generate large-scale instances of up to 500 customers, we focus our experiments on sizes ranging from 5 to 100 customers. Unlike static benchmark suites, SynthCharge integrates instance geometry with adaptive energy capacity scaling and range-aware charging station placement. To guarantee structural validity, the generator systematically filters out unsolvable instances through a fast feasibility screening process. Ultimately, SynthCharge provides the dynamic benchmarking infrastructure needed to systematically evaluate the robustness of emerging neural routing and data-driven approaches.
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