The time-definite hub line location problem
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Bibliographic record
Abstract
This paper introduces the Time-Definite Hub Line Location Problem (TD-HLLP), an innovative extension to the Hub Line Location Problem (HLLP) which aims to capture the time restrictions usually observed in delivery time on real logistics settings. To address this important gap in existing Hub Line models proposed in the literature, we propose a novel formulation that relies on a set-packing approach to enhance efficiency in time-sensitive deliveries. We conduct a thorough numerical analysis of the TD-HLLP behavior under various operational scenarios to explore the intertwined relationship between the number of vehicles performing the inter-hub transportation, the service level (i.e., the promised longest delivery time), and the network cost. Our results provide valuable information to regional transport managers who commit themselves to service constraints on delivery times. • The Time-definite Hub-Line Location Problem (TD-HLLP) shows how time constraints reshape hub and spoke allocation decisions. • Two inter-hub vehicle configurations significantly reduce delivery times compared to one vehicle. • Stricter service levels can force suboptimal hub placements to meet time limits. • TD-HLLP ensures optimal network designs for healthcare and logistics contexts. • Equilibrium points reveal when additional vehicles no longer improve efficiency.
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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 | 0.001 | 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 | 0.001 | 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