An efficient genetic algorithm for the uncapacitated single allocation hub location problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hub location problem is a NP-hard problem that frequently arises in the design of transportation and distribution systems, postal delivery networks, and airline passenger flow. We propose a simple but effective genetic algorithm (GA) for the uncapacitated single allocation hub location problem (USAHLP). Our main contribution is two new simple chromosome encoding schemes based on indirect representation and two crossover operators. We performed an empirical study to evaluate the effectiveness of the proposed GA using well-known benchmark problems from the Civil Aeronautics Board (CAB) and Australian Post (AP) data sets. The GA found all best-known solutions for the 80 CAB problems and introduced new solutions for the larger problem instances for AP data. The proposed GA can easily be extended to other variants of location problems arising in network design planning in transportation and distributed systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it