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cvrp_10_multi_depot_multi_vehicule_capacities
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cvrp_1000_multi_depot_multi_vehicule_capacities.npz
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cvrp_50_single_depot_multi_vehicule_capacities
cvrp_50_single_depot_multi_vehicule_capacities.npz
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cvrp_50_single_depot_multi_vehicule_capacities.npz
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cvrp_50_single_depot_multi_vehicule_capacities
cvrp_50_single_depot_multi_vehicule_capacities.npz
8
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cvrp_50_single_depot_multi_vehicule_capacities
cvrp_50_single_depot_multi_vehicule_capacities.npz
9
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cvrp_100_single_depot_multi_vehicule_capacities
cvrp_100_single_depot_multi_vehicule_capacities.npz
0
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cvrp_100_single_depot_multi_vehicule_capacities
cvrp_100_single_depot_multi_vehicule_capacities.npz
1
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cvrp_100_single_depot_multi_vehicule_capacities
cvrp_100_single_depot_multi_vehicule_capacities.npz
2
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cvrp_100_single_depot_multi_vehicule_capacities
cvrp_100_single_depot_multi_vehicule_capacities.npz
3
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cvrp_100_single_depot_multi_vehicule_capacities
cvrp_100_single_depot_multi_vehicule_capacities.npz
4
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cvrp_100_single_depot_multi_vehicule_capacities
cvrp_100_single_depot_multi_vehicule_capacities.npz
5
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cvrp_100_single_depot_multi_vehicule_capacities
cvrp_100_single_depot_multi_vehicule_capacities.npz
6
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cvrp_100_single_depot_multi_vehicule_capacities
cvrp_100_single_depot_multi_vehicule_capacities.npz
7
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cvrp_100_single_depot_multi_vehicule_capacities
cvrp_100_single_depot_multi_vehicule_capacities.npz
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cvrp_100_single_depot_multi_vehicule_capacities
cvrp_100_single_depot_multi_vehicule_capacities.npz
9
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🚚 SVRPBench

SVRPBench is an open and extensible benchmark for the Stochastic Vehicle Routing Problem (SVRP). It includes 500+ instances spanning small to large scales (10–1000 customers), designed to evaluate algorithms under realistic urban logistics conditions with uncertainty and operational constraints.

πŸ“Œ Overview

Existing SVRP benchmarks often assume simplified, static environments, ignoring core elements of real-world routing such as time-dependent travel delays, uncertain customer availability, and dynamic disruptions. Our benchmark addresses these limitations by simulating urban logistics conditions with high fidelity:

  • Travel times vary based on time-of-day traffic patterns, log-normally distributed delays, and probabilistic accident occurrences
  • Customer time windows are sampled differently for residential and commercial clients using empirically grounded temporal distributions
  • A systematic dataset generation pipeline that produces diverse, constraint-rich instances including multi-depot, multi-vehicle, and capacity-constrained scenarios

πŸ“¦ Dataset Components

The dataset includes various problem instances:

  • Problem sizes: 10, 20, 50, 100, 200, 500, 1000 customers
  • Variants: CVRP (Capacitated VRP), TWCVRP (Time Window Constrained VRP)
  • Configurations: Single/Multi-depot, Single/Multi-vehicle

Each instance includes:

  • Customer locations
  • Demand volumes
  • Time window constraints
  • Vehicle capacity limits
  • Depot coordinates

πŸ§ͺ Supported Algorithms

The benchmark includes implementations of several algorithms:

  • OR-tools (Google's Operations Research tools)
  • ACO (Ant Colony Optimization)
  • Tabu Search
  • Nearest Neighbor with 2-opt local search
  • Reinforcement Learning models

πŸ“Š Benchmarking Results

Results compare algorithm performance across different problem sizes:

Model CVRP10 CVRP20 CVRP50 CVRP100 CVRP200 CVRP500 CVRP1000
OR-tools 1.4284 1.6624 1.3793 1.1513 1.0466 0.8642 -
ACO 1.5763 1.7843 1.5120 1.2998 1.1752 1.0371 0.9254
Tabu 1.4981 1.7102 1.4578 1.2214 1.1032 0.9723 0.8735
NN+2opt 1.6832 1.8976 1.6283 1.3844 1.2627 1.1247 1.0123

πŸ› οΈ Usage

# Example of loading a dataset
from datasets import load_dataset
ds = load_dataset("MBZUAI/svrp-bench", split="test")
ds[0]

Sample

{'appear_times': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
 'demands': [0, 33, 52, 35, 85, 77, 68, 17, 61, 32, 23],
 'file_name': 'cvrp_10_multi_depot_multi_vehicule_capacities.npz',
 'instance_id': 0,
 'locations': [[523, 497],
               [394, 344],
               [536, 599],
               [341, 412],
               [734, 652],
               [492, 569],
               [491, 238],
               [419, 787],
               [688, 422],
               [708, 490],
               [431, 454]],
 'num_vehicles': 13,
 'subset_name': 'cvrp_10_multi_depot_multi_vehicule_capacities',
 'vehicle_capacities': [40.0]}

πŸ”‘ Features

  • Comprehensive evaluation framework for VRP algorithms
  • Realistic travel time modeling with time-dependent patterns
  • Time window constraints based on empirical distributions
  • Support for multi-depot and multi-vehicle scenarios
  • Visualization tools for solution analysis
  • Extensible architecture for adding new algorithms

πŸ“š Citation

If you use this benchmark in your research, please cite:

@misc{svrbench2025,
  author = {Heakl, Ahmed and Shaaban, Yahia Salaheldin and TakÑč, Martin and Lahlou, Salem and Iklassov, Zangir},
  title = {SVRPBench: A Benchmark for Stochastic Vehicle Routing Problems},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/yehias21/vrp-benchmarks}}
}

πŸ“„ License

This project is licensed under the MIT License.

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