Multimode Resource-Constrained Scheduling and Leveling for Practical-Size Projects
Why this work is in the frame
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Bibliographic record
Abstract
This paper aims at providing a fast near-optimum solution to the multimode resource-constrained project scheduling problem (MRCPSP) in large-scale projects, with and without resource-leveling constraints. The MRCPSP problem is known to be nondeterministic polynomial-time hard (NP-hard) and has been solved using various exact, heuristic, and metaheuristic procedures. In this paper, constraint programming (CP) is used as an advanced mathematical optimization technique that suits scheduling problems. The IBM ILOG modeling software and its CPLEX-CP solver engine have been used to develop a CP optimization model for the MRCPSP problem. Unlike many metaheuristic methods in literature, the CP model is fast and provides a near-optimum solution to the MRCPSP for projects with hundreds of activities within minutes. The paper compares the CP results with two case studies from the literature to prove the practicality and usefulness of the CP approach to both researchers and practitioners. One case study was used as the basis for creating larger projects with up to 2,000 activities. The results reported in this paper can be used as a benchmark for other researchers to compare and improve. This research contributes to developing a practical decision support system for resolving real-life constraints in projects.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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