Part-Requirement Matrices for Reconfigurable Assembly Line Scheduling

5.5 KB of part-requirement matrices for different product types, supporting a study on reconfigurable assembly line scheduling. The dataset, authored by Haoyi Zhao and last updated in May 2026, was used to formulate a multi-objective mathematical model minimizing reconfiguration cost and balancing production workloads. A computational study on 120 generated benchmark instances demonstrated the performance of a proposed Q-learning-based hyper-heuristic algorithm.

Use Cases

• Benchmarking multi-objective optimization algorithms based on the described reconfiguration cost and workload metrics.
• Training reinforcement learning agents for adaptive operator selection in hyper-heuristics, as described with the Q-learning framework.
• Testing mathematical models for production scheduling that aim to minimize reconfiguration costs and equalize workloads.
• Comparing the performance of metaheuristic operators like particle swarm optimization and grey wolf optimizer within a unified search framework.

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• The associated study validates the data's utility across 120 generated benchmark instances.
• The dataset is openly licensed under CC-BY-4.0 for reuse.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Haoyi Zhao via figshare

Collection Method

Likely generated as part of a numerical case study for a scheduling optimization research project.

Freshness

Last updated 2026-05-21 07:32:00; freshness should be verified.