Multi-Project Multi-Mode Resource Constrained Scheduling Problem with Material Ordering
Bibliographic record
Abstract
In Multi–mode Project Scheduling with Resource Constrained (MPSRCP), activities are sequenced under resource limitation. In this thesis, an extension of the problem is considered. Multi-project multi-mode resource constrained scheduling problem with material ordering is studied. Bonus and penalty are taken into account in solving the considered problem as it is the case in many different industries. A literature review is presented and various solution methods for solving the considered and similar problems are studied. A new mathematical model is proposed considering a multi-project version of the problem. A new decomposition based heuristic to solve the problem is developed in this thesis. The approach is to use three separated mathematical models for each part of the problem. The developed heuristic is examined using various example problems with different features and randomly generated data. It can generate close-to-optimal solutions for all tested example problems with much reduced computational time when off-shelf optimization software was used. The developed math model and heuristic method are applied to a larger size case study based on a practical system in a manufacturing company in northern Ontario.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".