ASOC: A Novel Agent-Based Simulation-Optimization Coupling Approach-Algorithm and Application in Offshore Oil Spill Responses
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
The efficiency of offshore oil spill response not only relies on an efficaciously global decision/planning in devices combination and allocation, but also depends on the timely control for response devices (e.g., skimmers and booms). However, few studies have reported on such decision framework with a timely integration of global planning and operation control to support offshore oil spill response. This study developed an agent-based simulation-optimization approach to provide sound decisions for device combination and allocation during offshore oil spill recovery in a fast, dynamic and cost-efficient manner under uncertain conditions. Meanwhile, the proposed approach aimed at providing operation control schemes for different devices, reflecting the site conditions, and correspondingly adjusting the global planning in a real-time manner. Such functions would be extremely helpful in the harsh environments prevailing in offshore Newfoundland. In the case study, the developed approach was applied to determine the allocation of 3 responding vessels in collecting spilled oil at 7 locations. The routes of the responding vessels for response operation were optimized and reflected by the principle agent-based programming. Furthermore, several oil weathering processes (e.g., evaporation and dispersion) were also taken into account in the optimization. The modeling results indicated that a minimal timeframe of 21 hours was needed for vessel allocation and recovery operation, leading to an oil recovery rate of 90%. By taking evaporation and dispersion into account, the optimal time window was 18 hours, leading to an oil recovery rate of 75%, an evaporation rate of 12%, and a dispersion rate of 3%. The proposed approach can timely and effectively support the optimal allocation of devices, the control of operation, and the real-time adjustment of global decision making for oil recovery under dynamic conditions.
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 | 0.000 | 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 | 0.001 |
| 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