Optimal operations sequence retrieval from master operations sequence for part/product families
Bibliographic record
Abstract
This research capitalises on commonalities between members of a product family to increase the speed, consistency and efficiency of constructing a master operations sequence and optimal operations sequences for new variants. Two novel mixed integer programming (MIP) models are developed for generating master operations sequence based on available operations sequences of a family of part/product variants. The use of master operations sequence reduces the time, cost and effort required for developing new operations sequences, hence improving the planning efficiency and productivity. The first MIP model is developed for variants with serial operations sequence while the second is a generalised model for serial, networked operations sequences or a combination of both structures. The developed models generate master operations sequences which have minimum total dissimilarity distance from existing variants. The master operations sequence is then used to construct the operations sequence for new variants falling within or significantly overlapping with the boundary of the considered product family. As the number of operations increases, the efficiency of mathematical models decreases. Therefore, a novel algorithm is proposed to generate master operations sequences for product variants with any type of process sequence structure (i.e. serial, networked, or combination). Computational results demonstrated the capability of developed MIP algorithms to find optimum solutions and optimal operations sequence for new variants in a fraction of a second in most cases of small, medium and large size studied problems. Two assembly and fabrication case studies are provided for demonstration.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".