A multi-criteria analysis for the case of carriers having clear visibility into future demand for their freight delivery services
Bibliographic record
Abstract
Carriers providing truckload freight delivery services can find value in having visibility into future demand for their services to deliver shipments (a.k.a. loads). In the extant literature, the predicted value of this visibility is improved profitability for carriers. We extend this literature by showing that profit is not the only criterion for assessing this visibility (termed future load visibility (FLV) herein). Through extensive computational experiments, we model the effects of FLV on carrier profits as well as on three other criteria that matter to other stakeholders in freight transport ecosystems: (i) ecological consequences of freight transport; (ii) customer service for freight consignors/consignees; (iii) prices that consignors pay for freight delivery. In addition to providing a multi-criteria analysis of FLV, another major novelty of our work is in showing that the level of inter-carrier competition factors into how FLV affects the various criteria. A particularly significant insight from our investigation of situations involving such competition is the seemingly paradoxical finding that carriers’ FLV possession can sometimes impede better outcomes on non-profit criteria. This and other findings yield the paper’s central conclusion that while the decision to acquire FLV is more evidently justified on the profit criterion, it is not an unequivocally optimal decision when non-profit criteria are considered.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".