An Agent-based Simulation-optimization Coupling Approach for Device Allocation and Operation Control in Response to Offshore Oil Spills
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 study has reported on such decision framework with timely integration of global planning and operation control to support the offshore oil spill recovery. This study developed an agent-based simulation-optimization coupling approach to provide sound decisions for devices combination and allocation for offshore oil spill recovery in a fast, dynamic and cost-efficient manner under uncertain conditions. At the same time, the approach aimed at providing operation control for specific devices, reflecting the site conditions, and correspondingly real-time adjusting the global planning, which was especially helpful to harsh environments prevailing in the Newfoundland offshore areas. In the case study, the developed approach was applied to determine the allocation of 3 response vessels from 7 different locations of the spilled oil slicks. The routes of the response vessels for response operation were optimized and reflected by the principle agent-based programming. The modeling results indicated a minimal time of 21 hours for vessels allocation and recovery operation when only considered oil recovery, leading to an oil recovery rate of 90%. The proposed approach can timely and effectively support optimal allocation of devices and control of operation as well as real-time adjustment of global decision for oil recovery under dynamic conditions and improve recovery efficiency.
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 | 0.002 | 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".