An efficient hybrid heuristic method for prioritising large transportation projects with interdependent activities
Bibliographic record
Abstract
Transportation projects are generally large, with limited resources and highly interdependent activities. The complexities and interdependencies apparent in large transportation projects have prohibited effective application of management science and economics methods to these problems. We propose a heuristic method with several hybrid components. We formulate the problem as a Travelling Salesman Problem (TSP). A Neural Network (NN) is used to cope with the interdependency concerns. An algorithm with an iterative process is confined to search for the longest path (most benefit or most reduction in the user-time) in the NN as a solution to the TSP. The solution from each iteration step is utilised to update and train the NN and enhance its prediction. A search engine inspired by the concept of Ant Colony (AC) and hybridised with Genetic Algorithm (GA) is developed to find a suitable solution to the TSP. The hybrid heuristic method proposed in this study is applied to the real data for the city of Winnipeg in Canada to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".