Decision Support NSGA-II Optimization Method for Resource-Constrained Schedule Compression with Allowed Activity Splitting
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In the course of a construction project, the project manager’s task is to ensure timely and cost-effective execution of the job. However, it is common that delays and over-budgeting to be experienced during the project execution. This schedule acceleration requires resource planning to account for the project’s limited resources. Therefore, this study proposes an integrated method that allows for joint consideration of project scheduling and resource planning while accounting for activity splitting. The objective is to determine the project’s optimal cost and duration while considering some input parameters such as the crew’s size and project’s activities’ cost and duration. The proposed method utilized the Genetic Algorithm (GA) to optimize the project duration and cost. Accordingly, the Weighted Sum was used as a multi-criteria decision support method to choose an optimal solution from the optimization results. The developed scheduling and optimization method is coded in Python as a stand-alone, automated, computerized tool to facilitate its application. A numerical example, utilizing the developed method, is employed to show the method’s robustness and assess its performance against other previously developed methods. Results indicated the developed method’s dominance in finding optimal solutions in a reasonable time avoiding local minima entrapment.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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