MBZUAI/svrp-bench

🚚 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.

📌 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

📦 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

🧪 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

📊 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

🛠️ Usage

# Example of loading a dataset
from load_dataset import load_vrp_dataset

# Load a CVRP dataset with 50 customers, single depot configuration
dataset = load_vrp_dataset('cvrp', 50, 'single_depot')

# Run evaluation
from vrp_bench.vrp_evaluation import VRPEvaluator

evaluator = VRPEvaluator()
results = evaluator.evaluate_solver(solver_class=ACOSolver, 
                                   solver_name="ACO",
                                   sizes=[50, 100])

🔑 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

📚 Citation

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

@misc{svrbench2025,
  author = {Heakl, Ahmed and Shaaban, Yahia Salaheldin and Takáč, 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}}
}

📄 License

This project is licensed under the MIT License.