Multicriteria Optimization of A Long‐Haul Routing and Scheduling Problem
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Long‐haul carriers are facing a shortage of drivers in North American countries. To reduce turnover rates and improve driver retention, trucking companies are making more efforts to improve their drivers' quality of life. The aim of this paper is to introduce and solve a multi‐objective vehicle routing and truck driver scheduling problem under the legislative requirements on work and rest hours in the US (US MOVRTDSP). We present a tabu search algorithm that solves the US MOVRTDSP and provides a heuristic non‐dominated solution set from which tradeoffs between operating costs and driver inconvenience are evaluated. The tradeoffs between the number of vehicles used and operating costs are also estimated. Overall, interpretations of the computational results on artificial and real‐life instances provide meaningful information to long‐haul carriers. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it