Strategic expansion of freight transportation hub networks under demand uncertainty
Bibliographic record
Abstract
We focus on freight transportation carriers that transport shipments that are small relative to vehicle capacity and incur transportation costs that exhibit economies of scale. For such carriers, profitability is driven by shipment consolidation, which is achieved by routing shipments through a network of hubs. We consider a carrier that seeks to expand its network into new regions by merging with carriers that already operate in those regions. We focus on how, and by how much, the network that results from such a merger should be redesigned to maximize profitability. We perform a case study based on operations from a multi-regional United States Less-than-truckload freight transportation carrier to derive insights into the profitability of different redesign strategies. We derive insights into how a network that results from a merger should be redesigned. We also study how uncertainty in shipment sizes impacts the structure of the redesigned networks. • Studies redesign of merged transportation hub networks for maximum profitability. • Introduces three profit-maximizing capacitated hub location models. • Transportation costs between hubs are modeled on a per-vehicle basis. • Two different stochastic models address the impacts of shipment size uncertainty. • A case study is presented based on a multi-regional freight transportation carrier.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".