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 to optimize product sequencing, reconfiguration cost, workload equalization, and logistics leveling. 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 metaheuristic operators like particle swarm optimization.
• Evaluating adaptive operator selection strategies using Q-learning feedback.
• Studying scheduling solutions that balance reconfiguration cost, workload, and logistics.

Strengths

• Contains 120 generated benchmark instances for comparative algorithm evaluation.
• Dataset is small (5.5 KB), facilitating quick download and inspection.
• Released under a permissive CC-BY-4.0 license.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset's small size (5.5 KB) suggests it contains 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 07:31:57; freshness should be verified.