A Game Theoretic Approach for the Real-Life Multiple-Criterion Vehicle Routing Problem With Multiple Time Windows
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a new framework for the optimization of real-life multiple-objective vehicle routing problems with multiple time windows. This framework uses a hybrid variable neighborhood tabu search heuristic that chooses Pareto nondominated solutions from the search space of solutions that satisfy a set of Nash equilibrium conditions for a multiple-agent game theory model. Even though the framework is general and can tackle different classes of vehicle routing problems, it is herein tested on three objectives: minimizing the total travel cost (expressed in time units), maximizing the minimal customers’ utility, and maximizing the minimal drivers’ utility. The results on real-life instances provided by a Canadian transportation company highlight the benefits of the multiple-criteria model; an important motivation to the transportation industry for its real-life implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 | 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