Operations Research Benchmark: Combinatorial Optimization and Cooperative Driving Problems

An operations research benchmark adapted from ReEvo, which originally consists of six types of combinatorial optimization problems. The benchmark has been extended with a cooperative driving problem involving complex simulation environments using SUMO. The dataset was created by qiliuchn and last updated on April 3, -2026.

Use Cases

• Benchmarking optimization algorithms based on the six classical combinatorial optimization problems.
• Developing and testing cooperative driving strategies based on the SUMO simulation environment.
• Evaluating agent performance on complex, multi-objective problems based on the described function categories.

Strengths

• Extends a known benchmark (ReEvo) with a new, complex simulation-based problem.
• Last updated on 2026-04-03, indicating recent maintenance.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.

Provenance

Source

Adapted from ReEvo and extended by the author.

Collection Method

Likely compiled from established combinatorial optimization problems and custom simulations.

Time Range

null

Freshness

Last updated 2026-04-03 13:55:27.

Geography

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