A Heuristic Algorithm for a Robust Resource-Constrained Project Scheduling Problem with Multi-Skilled Resources
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
This paper studies a resource-constrained project scheduling problem with stochastic activity durations and multi-skilled resource constraints. Robust project scheduling is employed to tackle uncertainty. We name it the robust resource-constrained project scheduling problem with multi-skilled resources (RRCPSP-MR). The objective is to schedule the starting times of activities and allocate multi-skilled resources reasonably in order to maximize the robustness of the project schedule in the presence of activity duration variability. An optimization model is constructed to formulate this problem. Based on the NP-hardness attribute of the problem, a resource allocation heuristic algorithm is developed to obtain satisfactory solutions. In addition, a demonstration case is executed to show the problem clearly and verify the effectiveness of the proposed model and algorithm. It renders further proof that multi-skilled attributes of resources can improve the robustness of baseline schedules.
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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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