Designing hub-based regional transportation networks with service level constraints
Bibliographic record
Abstract
• Three hub network topologies analyzed for time-definite constraints. • Extended formulations integrate time-definite service level constraints. • Validated through Quebec’s Integrated Centers of Healthcare case study. • Highlights the importance of hub structure for meeting service levels. • Novel set-packing formulation for efficient double-path routing. This paper investigates the suitability of hub-based structures for coping with so-called regional transportation networks, i.e., transportation structures able to connect any two nodes in the network respecting a tight service level requirement. While hub-and-spoke structures have been extensively studied in the literature, they were typically approached as strategic problems with minimal focus on incorporating service-related constraints. This contrasts with current trends in regional transportation that request greater flexibility and stricter service schedules. We analyze how the requirement of time-definite constraints impacts three different hub-based network topologies, including a new structure proposing a double-path route to connect the inter-hubs traffic. To this end, we extend previous formulations to cope with the mentioned time-definite constraints, and we propose a new set-packing formulation for the one that connects the hubs by a double-path route. Numerical experiments based on realistic instances inspired by Quebec’s Integrated Centers of Healthcare and Social Services (CISSS) allowed us to evaluate the performance of the proposed configurations, demonstrating the critical importance of selecting the appropriate hub network structure to meet targeted service levels. We provide valuable insights for managers aiming to redesign their regional transportation networks with an emphasis on service level and synchronization.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".