SynthCharge: An Electric Vehicle Routing Instance Generator with Feasibility Screening to Enable Learning-Based Optimization and Benchmarking
2026-03-03 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors address the problem of planning routes for electric vehicles that need to consider battery limits and charging stops, called EVRPTW. They note that existing test datasets are static and may include impossible scenarios, making it hard to fairly test new routing models. To fix this, they created SynthCharge, a tool that generates many different, realistic route planning problems while filtering out those that can't be solved. Their tool can create large cases but they focused on medium sizes in their experiments. This helps researchers better test and compare routing methods that use learning or data-driven approaches.
Electric Vehicle Routing Problem (EVRPTW)Vehicle Routing Problem with Time Windows (VRPTW)Battery Capacity ConstraintsCharging Station PlacementBenchmark DatasetFeasibility ScreeningInstance GenerationNeural Routing ModelsData-Driven RoutingSynthetic Data
Authors
Mertcan Daysalilar, Fuat Uyguroglu, Gabriel Nicolosi, Adam Meyers
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 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.