Integration of functional resonance analysis method and reinforcement learning for updating and optimizing emergency procedures in variable environments

Xuan Liu, Huixing Meng, Xu An and Jinduo Xing

Reliability Engineering and System Safety, 2024, vol. 241, issue C

Abstract: Blowout accidents are prone to generate personal casualties, property losses, and even environmental disasters. To alleviate the consequences of accidents, it is essential to conduct effective emergency operations and update emergency schemes when necessary. In the update of the emergency plan, how to effectively optimize the allocation of resources is an open question. To deal with above difficulties, we propose a hybrid methodology by integrating the functional resonance analysis method (FRAM) and reinforcement learning (RL) for updating and optimizing emergency schemes. In the proposed methodology, FRAM is utilized to model the emergency response process based on function, variability, and coupling. Since the environment of emergency operations usually changes, RL is introduced to update emergency schemes that are constructed by FRAM. The selection of reward value by the agent reflects the variability of functional nodes in the FRAM model. To optimize emergency schemes, the interval analytic hierarchy process is integrated with multi-objective decision-making to analyze the duration, cost, and exposure risk of emergency operations. The installation of a capping stack, an emergency technique for deepwater blowout accidents, is employed to illustrate the applicability of the methodology. The results show that the proposed model is beneficial to determine emergency actions adapted to condition or scenario change in accidents.

Keywords: Emergency scheme; Functional resonance analysis model; Reinforcement learning; Multi-objective decision making (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005690

DOI: 10.1016/j.ress.2023.109655

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