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---
license: mit
language:
- en
tags:
- Optimization
- combinatorics
- Vehicle Routing Problem
- SVRP
- Logistics
- Transportation
pretty_name: SVRPBench Dataset
size_categories:
- 10K<n<100K
---
# π SVRPBench
SVRPBench is an open and extensible benchmark for the Stochastic Vehicle Routing Problem (SVRP). It includes 500+ instances spanning small to large scales (10β1000 customers), designed to evaluate algorithms under realistic urban logistics conditions with uncertainty and operational constraints.
## π 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
## π¦ 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
## π§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
## π 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 |
## π οΈ Usage
```python
# Example of loading a dataset
from datasets import load_dataset
ds = load_dataset("MBZUAI/svrp-bench", split="test")
ds[0]
```
### Sample
```json
{'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]}
```
## π 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
## π Citation
If you use this benchmark in your research, please cite:
```bibtex
@misc{svrbench2025,
author = {Heakl, Ahmed and Shaaban, Yahia Salaheldin and TakΓ‘Δ, 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}}
}
```
## π License
This project is licensed under the MIT License.
- π Paper: [Arxiv](https://arxiv.org/abs/2505.21887) |