A hybrid meta-heuristic algorithm for solving real-life transportation network design problems
Bibliographic record
Abstract
The network-design problem (NDP) has a wide range of applications in transportation, telecommunications, and logistics. The idea is to efficiently design a network of links (roads, optical fibres, etc.) enabling the flow of commodities (drivers, data packets, etc.) to satisfy demand characteristics. Various exact and heuristic methods such as branch and bound, Tabu search, genetic algorithm (GA), ant system (AS) have been developed to address the NDP which is a highly intractable combinatorial problem. The literature has yet to address the NDP in real-size networks. In this study, we propose a new meta-heuristic algorithm for solving large NDPs by hybridising GA and AS methods. The applicability of the proposed meta-heuristic approach to real-size networks is demonstrated at two different sites. First, we use a large real-life problem for the city of Winnipeg, Canada and show that our heuristic method produces exact solutions very efficiently. Second, we evaluate the performance of the proposed algorithm using the data of Sioux Falls (a benchmark in the literature). While the proposed approach produces solutions similar to the other available methods in the literature, it is superior for developing solutions in large-size NDPs.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".