Facing robustness as a multi-objective problem: A bi-objective shortest path problem in smart regions
Bibliographic record
Abstract
The goal in Robust Optimization is to optimize not only the quality of the solutions but also the variation of this quality with the uncertain parameters of the optimization problem . We propose a robust model for the bi-objective shortest path problem applied in a smart mobility context: Finding routes for cars in a city to minimize travel time and gas emissions. Our proposal treats robustness from a multi-objective point of view. We model the parameters that define each instance as random variables , described through their mean and variance. In this way, we can obtain efficient solutions that are also less sensitive to changes in the environment. We run different types of algorithms in multiple instances to solve this problem so that we obtain a global view of the behavior of different techniques. All experimentation uses a scenario based on real data : The province of Malaga, Spain. This realistic settlement for our study allows us to test the applicability of our model in final systems for the citizens. The results clearly state the interest of our proposal for tackling robustness and represents a new state-of-the-art in smart mobility, an always appealing feature of works, that could lead to an industrial prototype.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".