Optimizing Modular Hub Location in Air and Road Transportation Systems
Bibliographic record
Abstract
Hub networks play a crucial role in optimizing transportation costs in air and road systems. Their main objective is to strategically locate hubs and allocate non-hub nodes within the network. The modular hub location problem is a specific area of hub network design that focuses on accurately calculating transportation costs, considering factors like trip numbers and capacity constraints in network routes. This study proposes a mixed-integer programming model to address the modular hub location problem with multiple allocations. It considers dependent and independent costs associated with vehicles per trip between hub network routes, considering specific vehicle capacities. Two datasets are utilized for validation: the CAB dataset representing 25 nodes of US airports and the TR dataset representing the Turkish transportation system with 81 nodes. To tackle the NP-hard nature of hub location models and the computational complexity of the proposed model, two solutions are developed. Firstly, a novel LP relaxation-based method using GAMS software provides near-optimal solutions for medium-sized instances. Additionally, a Genetic Algorithm (GA) implemented in MATLAB handles larger instances. The GA's efficiency is enhanced by tuning its parameters using the Taguchi method. Results analysis shows that both proposed algorithms yield high-quality solutions within significantly reduced timeframes compared to the CPLEX solver in GAMS software. The LP relaxation-based method performs well for medium-sized instances, while the GA approach is efficient for larger instances after parameter tuning with the Taguchi method.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
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".