An Open Access Journal
Table 5 Open-access datasets, test instances, and software
Reference | Feature | # of instances | Link |
|---|---|---|---|
Datasets/test instances/solutions | |||
Schneider et al. [24] | E-VRPTW, widely used datasets for different variants of VRP and location-routing problems using EVs | A set of small instances (5, 10, and 15 customers) and a set of large instances (up to 100 customers and 21 charging stations) | |
Mendoza et al. (2014) | Vehicle-routing problem repository (VRP-REP) | ||
Felipe et al. [77] | Green vehicle routing problem with multiple technologies and partial recharges | 3 sets of 20 instances with 100, 200, and 400 customers | |
Bongiovanni [47] | Uber ride-share data from San Francisco | https://github.com/dima42/uber-gps-analysis/tree/master/gpsdata | |
Bongiovanni [47] | EAV-DARP instances | Two datasets adapted from Cordeau [95] and the Uber dataset mentioned above,each contains 14 instances (up to 5 vehicles and 50 customers) and solutions for the Uber test dataset | https://luts.epfl.ch/wpcontent/uploads/2019/03/e_ADARP_archive.zip |
Kullman et al. [54] | Ride-hailing data from New York City | https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page | |
Yu et al. [55] | Trip data from Didi Chuxing ride-hailing services | ||
Froger et al. [78] | Test instances for EVRP with non-linear charging functions and capacitated stations | 120 instances from Montoya et al. [20] | |
Kucukoglu et al. [14] | Summary of the best exact and heuristic solutions to date for E-VRPTW | ||
Software | |||
Kullman et al. [79] | Python package | Exact labeling algorithm to solve the fixed-route vehicle-charging problem | |
Pessoa et al. [80] | Open-access solver developed by a research group at the University of Bordeaux | Branch-cut-and-price-based exact solver for VRP-related problems, interface implemented in Julia and JuMP package | |