Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty

RWTH Aachen University provides test instances for robust combinatorial optimization with budget uncertainty. The set contains nominal problems from MIPLIB 2017 converted into robust problems, instances of the robust knapsack problem, and instances for robust weighted matching and independent set problems. These instances were created by Christina Büsing, Timo Gersing, and Arie Koster for benchmarking algorithms described in published papers.

Use Cases

• Benchmarking robust optimization algorithms based on the described knapsack and MIPLIB-derived problems.
• Testing algorithm performance on robust weighted matching problems based on bipartite graphs.
• Evaluating methods for robust weighted independent set problems based on DIMACS challenge graphs.
• Comparing solution methods for robust binary optimization with budgeted uncertainty.

Strengths

• Instances are derived from the established MIPLIB 2017 benchmark library.
• Instances are linked to specific, peer-reviewed papers in Mathematical Programming Computation and IPCO.
• Covers multiple classic combinatorial problems like knapsack, matching, and independent set.

Limitations

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

Provenance

Source

RWTH Aachen University

Collection Method

Converted from MIPLIB 2017 problems and graphs from the DIMACS implementation challenge.