Minimising makespan and total tardiness in no-wait open-shop scheduling problems using metaheuristic algorithms: a narrative review
Bibliographic record
Abstract
This study begins with a comprehensive narrative review of shop layout and task scheduling, establishing its novelty by being the first to apply an open-shop nonlinear methodology to four metaheuristic algorithms. The research addresses the no-wait open-shop scheduling problem (NWOSP) through a mixed integer nonlinear problem (MINLP) framework, focusing on minimising completion time (Makespan) and total tardiness while considering machine availability, job sequencing, and machine-to-machine transfer durations influenced by job types. An innovative transportation method with unlimited capacity eliminates delays. Comparative analysis reveals that particle swarm optimisation (PSO) and simulated annealing (SA) consistently outperform other algorithms, while Harris Hawks optimiser (HHO) and genetic algorithm (GA) show competitive performance in specific cases. The study integrates bi-objectives using reference point programming with Euclidean distances (RPPED) into a single nonlinear objective, contributing to operations research (OR) with streamlined optimisation processes, enhancing practical applications for complex scheduling problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".