Benchmark Instances for Reconfigurable Assembly Line Scheduling Optimization

pone.0348884.t011 contains benchmark instances for a reconfigurable assembly line scheduling problem. The dataset supports a study proposing a Q-learning-based multi-objective hyper-heuristic algorithm. It was authored by Haoyi Zhao and last updated on 2026-05-21.

Use Cases

• Benchmarking multi-objective optimization algorithms based on the described scheduling problem.
• Testing hyper-heuristic frameworks that integrate metaheuristics like particle swarm optimization or grey wolf optimizer.
• Evaluating scheduling solutions that aim to minimize reconfiguration cost and balance production workload.
• Studying the performance of Q-learning for adaptive operator selection in combinatorial optimization.

Strengths

• The dataset is associated with a computational study on 120 generated benchmark instances.
• It supports a formulated multi-objective mathematical model for a specific manufacturing scheduling problem.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

Haoyi Zhao via figshare

Collection Method

Generated benchmark instances for a computational study.

Freshness

Last updated 2026-05-21 07:31:58; freshness should be verified.