Multiobjective facility location model for optimizing Arctic oil spill response
Bibliographic record
Abstract
Escalating Arctic shipping necessitates preparedness for marine oil spill, highlighting strategic facility location and resource allocation decisions. Insufficient decision support tools (DSTs) may lead to suboptimal responses, impacting the transportation system. Although several facility location models are available, none have considered the Canadian Arctic’s unique circumstances, such as the vulnerability of marine protected areas and remote location. Further investigation is needed for determining number, location and allocation of facilities and resources in the Canadian Arctic. To fill these gaps, an Integer Programming hierarchical multiobjective optimization model is developed. The model contains two objectives: maximizing spill coverage – considering spill size, environmental sensitivity, response time – and minimizing associated facility development and variable costs. Findings demonstrate models’ suitability in terms of mean response time, coverage percentage and incurred cost. The proposed optimization model suggests building two new facilities incurring approximately 11 million dollars. Spill coverage is improved by 14% compared to existing setups, and mean response time is reduced from 9.8 h to 7.7 h. While this study assists decision-making, several limitations are highlighted and future research directions are outlined.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".