Multi‐objective maritime vessel routing with safety considerations
Bibliographic record
Abstract
Abstract The routing of maritime vessels is a challenging optimization problem that involves finding an adequate balance between conflicting and multiple objectives. This paper proposes a methodology based on inverse optimization to find appropriate objective weights that account for conflicting objectives. To formulate the inverse optimization problem, we integrate a weighted multi‐objective function in a model where duality for a network formulation and Karush–Kuhn–Tucker optimality conditions are used as key components. The objective includes route time, fuel consumption, and multiple safety considerations including dynamic stability, the probability of bow slamming, and green water occurrences. The motivation behind our choice of approach lies in the complexity of determining objective weights in multi‐objective problems and the need for incorporating the preferences of multiple stakeholders. To test the proposed approach, we use “best practice routes” based on expert knowledge, real‐world weather data, and domain‐specific objective analysis. Within the scope of this study, these routes are generated using an optimization model with predefined objective weights applied to evaluate the efficacy of the approach. The results demonstrate that the proposed inverse optimization model identifies the weights associated with the best practice routes. A comparison with the analytic hierarchy process (AHP) shows that inverse optimization produces routing decisions more closely aligned with expert‐defined best practice routes, while AHP introduces discrepancies in fuel consumption and travel time, leading to suboptimal routing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".