Pareto Solutions for a Reconfigurable Assembly Line Scheduling Model

A set of Pareto-optimal solutions achieved by a multi-objective mathematical model for reconfigurable assembly line scheduling. The dataset, shared by Haoyi Zhao on figshare, was last updated on 2026-05-21. It contains results from a numerical case study and computational experiments on 120 benchmark instances.

Use Cases

• Benchmarking multi-objective optimization algorithms based on the 120 generated benchmark instances.
• Analyzing trade-offs between reconfiguration cost, workload equalization, and logistics leveling based on the described scheduling objectives.
• Testing hyper-heuristic frameworks based on the integration of particle swarm optimization, teaching–learning-based optimization, whale optimization, and grey wolf optimizer.
• Evaluating adaptive operator selection strategies based on the Q-learning mechanism described.

Strengths

• The dataset is derived from a computational study on 120 generated benchmark instances.
• The underlying model addresses three specific objectives: reconfiguration cost, production workload equalization, and logistics leveling.
• The dataset is small at 5.5 KB, facilitating quick download and inspection.

Limitations

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

Provenance

Source

Haoyi Zhao via figshare

Collection Method

Generated by a numerical case study and computational experiments using a proposed Q-learning-based multi-objective hyper-heuristic algorithm.

Freshness

Last updated 2026-05-21 07:32:02; freshness should be verified.