Expert-Decision-Based Scheduling Strategies for Construction Projects Management
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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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".