Zero-One Programming Approach to Determine Optimum Resource Supply under Time-Dependent Resource Constraints
Bibliographic record
Abstract
Skilled labor is critical to any construction project. The determination of optimum resource supply quantities over different project time periods compounds the resource-constrained project scheduling problem, which has yet to be formally formulated and analytically solved. Previous related research endeavors focused on the allocation of finite quantities of resources in order to arrive at the shortest total duration for a project. The proposed mathematical model is based on the modeling strategy underlying the zero-one programming approach, aiming to generate the optimum resource-constrained schedule under time-dependent resource constraints. Furthermore, a two-stage solution framework is devised to align with critical decision-making processes in current practices of project scheduling and workface planning. The resulting optimum schedule shortens total project duration while streamlining resource supply for each specified time period. An industrial turnaround project serves as the test bed to (1) demonstrate the effectiveness and computational efficiency of the proposed resource scheduling approach; and (2) identify the optimum time-dependent resource availability limits in practical application settings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".