A new MIP RCCP model for tackling tactical project planning
Bibliographic record
Abstract
This paper proposes a new continuous-time linear mixed model for rough cut capacity planning (RCCP) adapted to tackle various tactical project planning scenarios. RCCP models are typically designed for the early phases of projects to decide the work package's execution intensities for each project planning period. The goal is to solve large-scale, complex instances while ensuring both optimality and efficient resolution times. We propose modifications to the constraints of one of the best models in this field to improve its resolution. Recognizing that overlap between two consecutive periods adversely affects the performance of the MIP solver, we introduce minimal, fictitious boundaries between these periods. Additional strategies, derived from analyzing initial constraints and solver behaviour, ensure solution optimality. The objectives considered are: minimising the costs of using external resources by setting the project's end date (Time-driven) and minimising the makespan (Resource-driven). The bi-objective case is also addressed. The improved model reduces the average number of dual simplex iterations of CPLEX by 72%. Moreover, only 0.7% of large instances remained unresolved with the new model compared to nearly 30% of instances with the previous best model. The fast resolution of a single-objective problem opens up opportunities for multi-criteria approaches, making the model adaptable to more complex needs in practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".