Vehicle Routing Problem with Drones: 112 Instances for Logistics Optimization

David Sacramento Lechado, David Pisinger, and Stefan Røpke published 112 computational instances for the Vehicle Routing Problem with Drones in Transportation Research Part C. The data includes scenarios with varying customer counts, grid dimensions, and clustered or sensitivity analysis configurations. Each instance file contains customer coordinates and demand, with a separate file detailing truck and drone operational parameters like speed, capacity, and service times.

Use Cases

• Benchmarking metaheuristic algorithms like Adaptive Large Neighborhood Search based on the provided instance structure.
• Analyzing the impact of drone endurance and speed parameters on delivery route optimization.
• Studying delivery cost trade-offs between truck-only and combined truck-drone scenarios using the provided cost factors.
• Conducting sensitivity analysis on routing solutions using the dedicated 100-instance subset.
• Investigating the effect of customer spatial clustering on delivery efficiency using the 10 clustered instances.

Strengths

• Includes 112 core instances plus 485 additional instances for sensitivity and grid-size experiments.
• Instance naming convention (n.m.t) clearly encodes the number of customers and grid dimension.
• Provides a dedicated RouteData file with 13 specific operational parameters for trucks and drones.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Paper: 'An Adaptive Large Neighborhood Search Metaheuristic for the Vehicle Routing Problem with Drones' by David Sacramento Lechado et al.

Collection Method

Originally presented as computational instances in the academic paper.