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A dataset for two-echelon electric vehicle routing problems.
Mehmet Anıl Akbay1, Christian Blum1
1Artificial Intelligence Research Institute (IIIA-CSIC), Campus of the UAB, Bellaterra, 08193, Spain.
Data in Brief
|April 11, 2025
Summary
Researchers have developed a new dataset for Two-Echelon Electric Vehicle Routing Problems (2E-EVRPs) with complex constraints like time windows and partial deliveries. This dataset aids in testing advanced routing algorithms for electric vehicles.
Area of Science:
- Operations Research
- Transportation Science
- Logistics Management
Background:
- The Two-Echelon Vehicle Routing Problem (2E-VRP) is a complex logistical challenge.
- Electric Vehicle Routing Problems (EVRPs) introduce additional constraints related to battery capacity and charging.
- Existing datasets may not adequately capture the multifaceted nature of 2E-EVRPs with advanced operational constraints.
Purpose of the Study:
- To introduce a novel, comprehensive dataset specifically designed for Two-Echelon Electric Vehicle Routing Problems (2E-EVRPs).
- To provide a standardized benchmark for evaluating algorithms addressing complex routing scenarios.
- To facilitate research on electric vehicle logistics under realistic operational conditions.
Main Methods:
- Dataset generation based on established VRP and EVRP benchmark instances.
- Integration of satellite depot concepts and electric vehicle-specific constraints.
- Inclusion of diverse customer scenarios: time windows, simultaneous pickup and delivery (SPD), and partial deliveries.
- Generation of instances with varying sizes (5-100 customers) and geographical distributions (clustered, random).
Main Results:
- A versatile dataset encompassing a wide range of 2E-EVRP scenarios.
- Instances designed to test the robustness and scalability of routing algorithms.
- The dataset supports the evaluation of algorithms under single, SPD, and partial delivery conditions.
Conclusions:
- The introduced dataset serves as a valuable resource for advancing research in 2E-EVRPs.
- It enables rigorous testing and validation of new algorithms for electric vehicle logistics.
- The dataset promotes the development of more efficient and sustainable routing solutions.
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