Expert-Decision-Based Scheduling Strategies for Construction Projects Management
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an alternative methodology to the traditional Project Evaluation and Review Technique (PERT) by introducing the Dempster-Shafer theory of evidence into project scheduling. The evidence theory provides a flexible and robust framework for managing subjective beliefs, extending beyond PERT's rigid assumptions regarding activity durations and aligning more closely with the inherent uncertainties of real-world project environments. The proposed evidential reasoning approach enhances flexibility in estimating activity durations by leveraging expert judgment rather than relying on fixed probability distributions. Unlike PERT, which strictly assumes a beta distribution and fixed percentages for optimistic, most likely, and pessimistic estimates, this approach enables experts to adjust these percentages dynamically based on real-time project conditions. Through a detailed case study on a bridge construction project, this paper demonstrates the advantages of evidential reasoning in addressing limitations of traditional PERT. A comparative analysis validates the Dempster-Shafer theory's effectiveness, showing improved scheduling outcomes and adaptability.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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