A learnheuristic method for solving resource constrained project scheduling problem
Bibliographic record
Abstract
Project scheduling in resource-constrained mode is one of the most important issues in the field of project management. The main philosophy of this problem is to use less resources while respecting the resource limit to complete the project in a shorter time although other goals can be considered. When a very large amount of data is generated by the meta-heuristic algorithm and there are many variables involved in solving the problem, no other algorithm or technique is able to analyze the output. For this purpose, learnheuristics have the ability to use combined metaheuristics and machine learning tools with high accuracy and in less time to analyze data. The primary purpose of this research is to combine machine learning and genetic algorithms to reduce the project completion time which can lead to a reduction in the cost of the project. Due to the population-based nature of the problem a large amount of initial population was generated. In order to convert the generated schedules into feasible ones, a repair strategy was used. A data matrix was created to import data into the ML model. After specifying the training and testing settings of the model, the decision tree was used to analyze the data of the problem, then its output was applied to the initial population using the displacement or relocation procedure. This manipulated population is given to Genetic Algorithm (GA) and continues until a certain iteration. j60data on the PSPLIB website was used to evaluate the suggested approach. The findings indicate that the implemented approach has improved by 21.75% compared to the normal GA. This improvement means that a better solution could be achieved in less time with fewer calls.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".