An evolutionary stochastic discrete time-cost trade-off method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study introduces a newly developed method for optimized time-cost trade-off under uncertainty. It identifies the optimal execution mode for each project activity that results in minimizing the overall project cost and (or) duration while satisfying a specified joint confidence level of both time and cost. The method uses an evolutionary-based algorithm along with a design generator of experiments and blocking techniques. The developed method accounts for managerial flexibility towards the selection of execution modes. This accommodates experience-based judgement of project managers in this process. Hence, the second fold of the developed method is a completely randomized experiment module that depicts the main effect of changing an activity mode on the project total cost and overall duration. The method provides the decision-maker a guideline for making well-informed implementation strategies. The results obtained demonstrate benefits and accuracy of the developed method and its applicability for large-scale projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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