A review of methods, techniques and tools for project planning and control
Bibliographic record
Abstract
The purpose of this article is to provide a brief review of methods and techniques developed for the most commonly studied decision-making problems in project planning and control over the last decade. These problems involve project representation, project scheduling, resource allocation, risk analysis, time and cost performance evaluation, time, cost, and cash flow forecasting, optimal timing of control points, and corrective action decision-making. We also review recent tools developed for project planning and control. The emphasis is on recent contributions, but several older yet important works are also cited. Our analysis shows an increasing attention to the stochastic nature of projects in planning and control decision and processes. Recent attention has also been put at improvements in existing project control techniques as well as developing new methods to automate data collection, process, and generate more integrated project plan. More importantly, our review highlights an important shift in the project planning and control research field, which has been largely dominated by the project scheduling literature in the past, as short term and reactive decision-making bring new challenges and opportunities to project organisations and researchers.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".