Work

A dataset for two-echelon electric vehicle routing problems

Year: 2025

Type: data-paper

Abstract: This paper introduces a dataset generated for research on Two-Echelon Electric Vehicle Routing Problems (2E-EVRPs) with additional constraints, including time windows, simultaneous pickup and delivery (SPD), and partial deliveries. The dataset is derived from established benchmark instances from the VRP and EVRP literature and further extended using methodologies from the literature. It features diverse scenarios designed to challenge and validate solution approaches proposed for two-echelon routing algorithms under various constraints. The dataset comprises a variety of instances, ranging from small (5-15 customers) to large (100 customers), and incorporates different geographical configurations, including clustered, random, and random-clustered distributions. Key modifications include integrating satellite locations, enhanced vehicle configurations concerning electric vehicle (EV) constraints, and adjusting time windows to accommodate the two-echelon structure. The dataset also supports multiple delivery scenarios, allowing for single delivery, simultaneous pickups and deliveries (SPD), and partial deliveries, enabling researchers to test the performance of their algorithms across a range of realistic constraints.

Source: Data in Brief

DOI: 10.1016/j.dib.2025.111470

Authors: Mehmet Anıl Akbay, Christian Blum

Institutions: Consejo Superior de Investigaciones Científicas, Artificial Intelligence Research Institute

Language: English

Topic: Vehicle Routing Optimization Methods

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,220