Multiobjective Evolutionary Model of the Construction Industry Based on Network Planning
Bibliographic record
Abstract
The study of multiobjective optimization problems, such as resources, time limits, and cost based on network planning, can lead to an intelligent construction plan, which is very important for improving the comprehensive benefit of the project. In this article, a multiobjective static network planning optimization (i.e., sNP-RTC) model is proposed to integrate the heuristic local search algorithm and adaptive operation to improve the multiobjective particle swarm optimization (MOPSO) algorithm to solve the comprehensive “resource–duration–cost” optimization problem. The results show that this algorithm can achieve a unified and comprehensive Pareto frontier compared with the genetic MOPSO, providing a new method for solving resource–duration–cost optimization problems. Moreover, a dynamic “resource–time–cost” problem model for dynamic network planning (i.e., dNP-RTC) is presented and applied to improve the multiobjective subgroup algorithm to solve the model in real time after updating the network planning parameters. Compared with the static problem, the model increases the flexibility of problem solving, is more in-line with engineering practice, saves more cost in work scheduling, and has more application value for actual project scheduling.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".