Optimal resource allocation for hybrid flow shop in one-of-a-kind production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In one-of-a-kind production (OKP) companies, the number of production resources in each work centre of a production line is flexible in shifts because the resource demands of customer orders are always different from shift to shift. In this paper an optimisation model is established to achieve an optimal resource allocation plan ensuring that all jobs are finished in a given time interval with a minimum number of resources and without any buffer overflow. A branch-and-bound algorithm is developed to solve the problem. Two theorems are proved and applied in the algorithm to improve its pruning efficiency. A real industrial application is implemented for Gienow Windows and Doors Ltd. based on this model and algorithm. Experimental results show that this method is effective. Keywords: branch-and-boundhybrid flow shopone-of-a-kind production (OKP)resource allocation Acknowledgements This research is financially supported by the NSERC (Natural Sciences and Engineering Research Council) of Canada Strategic Program grant, NSERC Discovery grant, and the National Science Foundation of China (NSFC Proj. 70871020, 70721001 and 70625001).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it