ASOC: A novel agent-based simulation-optimization coupling approach-algorithm and application in offshore oil spill responses

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.