EXPLORING THE FRONTIER OF COMBINATORIAL OPTIMIZATION AND MACHINE LEARNING: APPLICATIONS TO VEHICLE ROUTING AND SUPPORT VECTOR MACHINES
Bibliographic record
Abstract
Combinatorial optimization (CO) is ubiquitous in myriad practical applications (e.g., production planning, scheduling, logistics, etc.). Over the years, CO and machine learning (ML) have emerged, together, as a prospective area of research for improving decision-making processes. There is interest to harness ML algorithms to improve existing CO methods. Conversely, since many ML tasks can be reformulated as optimization problems, there is broad interest in leveraging state-of-the-art CO methods for them. In this thesis, we conduct three studies that connect CO and ML around two important applications: the capacitated vehicle routing problem (CVRP) and support vector machines with hard-margin loss (SVM-HML). Our first study proposes a strategy to explore high-order local-search neighborhoods by pattern mining into two state-of-the-art metaheuristics for the CVRP. In a second study, also in the context of the CVRP, we exploit relatedness criteria for customer nodes using predictions from graph neural networks. We show that relatedness measures can be exploited to steer local search and extend crossover operators in a stateof-the-art genetic algorithm. Lastly, in a third study, we propose an efficient mixed-integer programming approach based on Combinatorial Benders cuts and sampling strategies for optimally training the SVM-HML.
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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 | 0.000 | 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 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".