pone.0348884.t009: Reconfigurable Assembly Line Scheduling Benchmark Instances

120 generated benchmark instances for a reconfigurable assembly line scheduling problem. The dataset supports a study proposing a Q-learning-based multi-objective hyper-heuristic algorithm and is provided by author Haoyi Zhao in an XLS file. It was last updated on 2026-05-21.

Use Cases

• Benchmarking multi-objective optimization algorithms based on the described reconfiguration cost, workload equalization, and logistics leveling objectives.
• Testing adaptive operator selection strategies in hyper-heuristics based on the described Q-learning framework.
• Evaluating population-based metaheuristics like particle swarm optimization or grey wolf optimizer on manufacturing scheduling problems.

Strengths

• Dataset includes 120 benchmark instances, providing a substantial test suite.
• The underlying study formulates a novel and linearized multi-objective mathematical model, suggesting a structured problem definition.
• Data is shared under a permissive CC-BY-4.0 license.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 5.5 KB file size indicates a very limited scope, likely containing only problem parameters or results, not extensive operational data.

Provenance

Source

Haoyi Zhao via figshare

Collection Method

Generated benchmark instances for a computational study.

Freshness

Last updated 2026-05-21 07:31:56; freshness should be verified.