Dynamic programming of 0/1 knapsack problem for network-level pavement asset management system
Bibliographic record
Abstract
For low volume roads, selecting maintenance and rehabilitation activities have become a major concern for most transportation agencies. At large-scale road networks, the combinatorial explosion of the feasible solution is a major difficulty with treatment activities programming. As part of the Wyoming Technology Transfer Center efforts to develop a comprehensive optimization analysis in Wyoming, this research study developed a user-friendly optimization algorithm, using a dynamic programming implementation of the 0/1 knapsack problem. The developed algorithm utilized the ant colony optimization algorithm, which is one of the swarm intelligence optimization sub-packages, to provide locally-optimal solutions (sub-sets) to select the optimal treatments for road networks at a large scale. Ultimately, a case study of 318 county roads in Wyoming is analyzed, and the results of different scenarios and current network performance are compared. The developed optimization algorithm helps the decision-makers in selecting the optimum set of roads that can maximize the overall pavement performance within a limited maintenance budget.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".