Designing Airline Hub-and-Spoke Network and Fleet Size by a Biobjective Model Based on Passenger Preferences and Value of Time
Bibliographic record
Abstract
This study presents a biobjective hub-and-spoke (HS) network design model for the global air passenger networks. The model explores the tradeoff between the total airline cost (airline preference) and lost time cost for passengers (user preference) as the model’s objectives. Most previous studies have focused on airline objectives and established HS networks based on the viewpoint of airlines, despite the importance of passenger objectives. Poor passenger service and inconvenience and dissatisfaction may lead to network breakdown. The major criteria for passenger dissatisfaction in HS networks are schedule and trip delays caused by nondirect flights. These delays (the lost time cost for passengers) are multiplied by the passengers’ value of time (VOT) and minimized as one of the model’s objectives. Another objective that is minimized is the transportation costs of the airline depending on the services provided (short-, medium-, and large-haul flights). The model is solved in a case study (Iranian Aeronautics Network) that is applied to the well-known yearbook of tourism statistics data. Pareto frontier was found for all candidate airports. Also, the number of aircraft required (short-, medium-, and large-haul), as well as the average load factor for different types of aircraft in various weights of the first objective (airline costs), was presented. The results of Pareto frontier indicated that Imam Khomeini International Airport should be selected as the global hub airport for Iran international flight network. Otherwise, Shiraz International Airport and Tabriz International Airport (as the first alternative), as well as Isfahan International Airport and Mashhad International Airport (as the second alternative), would be the best choices. The weight of the first objective (airline costs) seems to be between 0.7 to 1, a practical and logical weight that can reduce passenger costs (as the second objective) by 20% on average by adding only 15 long-haul, 40 medium-haul, and 37 short-haul aircraft to the airline’s fleet. Also, in this range, the average load factor for medium- and long-haul aircrafts is greater than 0.9, which seems to be ideal.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".