pone.0348884.t008: Reconfigurable Assembly Line Scheduling Benchmark Results

A figshare dataset by Haoyi Zhao, last updated on 2026-05-21. It contains results from a numerical case study and computational experiments on 120 generated benchmark instances for a reconfigurable assembly line scheduling problem. The study formulates a multi-objective mathematical model and proposes a Q-learning-based hyper-heuristic algorithm.

Use Cases

• Benchmarking multi-objective optimization algorithms based on the 120 generated instances.
• Analyzing Pareto solution sets for reconfigurable assembly line scheduling based on the described ε-constraint method results.
• Studying the performance of metaheuristic operators like particle swarm optimization within a hyper-heuristic framework.
• Investigating adaptive operator selection strategies using Q-learning for manufacturing optimization.

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• The underlying study compares the proposed methodology against nine other high-performing multi-objective algorithms.
• 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 dataset's 5.5 KB size suggests a very limited scope, likely containing summary results rather than raw experimental data.

Provenance

Source

figshare, author Haoyi Zhao.

Collection Method

Likely generated from computational experiments described in the associated study.

Freshness

Last updated 2026-05-21 07:31:55; freshness should be verified.