Decision Support NSGA-II Optimization Method for Resource-Constrained Schedule Compression with Allowed Activity Splitting
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".