The no-wait flow shop scheduling problem with sequence-dependent setup times: a comprehensive review and an application of the ALNS algorithm
Bibliographic record
Abstract
One of the most important decisions in any manufacturing company is how to schedule the operations on the available machines.In several industries, the nature of the job imposes certain constraints to operations scheduling.In a no-wait flow shop, once a job starts on the first machine, it has to continue being processed on the next ones, without any interruptions.As an extension of the flow shop scheduling, the no-wait version is also very difficult to be solved to optimality within a reasonable time, and many heuristics have been proposed for this problem.This work aims to classify existing solution algorithms proposed to solve the no-wait flow shop scheduling problem with setup times and some of its variants.We show how combining a heuristic to generate a good initial solution, local search procedures, insertion and swapping of job positions and techniques developed originally to solve transportation problems are among the popular and efficient techniques for the problem at hand.We also propose a new solution method based on the well-known Adaptive Large Neighborhood Search (ALNS) algorithm from transportation science.The use of this algorithm aims to minimize the total flow time as a performance measure.As this is a problem with high complexity, to achieve high solution quality in a reasonable time, an acceleration method was also adapted and applied into local search procedures with swapping operations.The results of the new method were compared to the best results in the literature for widespread instances, validating the quality of the method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".