Heuristic Method for Satisfying Both Deadlines and Resource Constraints
Bibliographic record
Abstract
Project deadline and resource limits are practical constraints that coexist in most projects. While heuristic methods for constrained resource scheduling (CRS) have become mainstream in commercial scheduling software, no commercial software includes any time-cost trade-off (TCT) heuristic to help meet deadline, let alone any procedure to resolve both deadline and resource constraints. This paper, therefore, introduces a practical heuristic method to meet both deadline and resource limits. The proposed method basically uses cycles of crashing for lowest-cost critical activities (i.e., stepwise TCT process) and resolves any resource overallocation (i.e., CRS) within each TCT cycle. This intertwined approach is logical, fast, and provides a set of feasible project durations that do not violate resource limits. To facilitate its practical use, the proposed method has been programmed as an add-in tool to Microsoft Project software. The paper discusses several case studies that prove the practicality and usefulness of the proposed approach to both researchers and professionals and provides a comparison of results with other literature efforts.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".