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Context‐based automation of critical infrastructure systems for efficiency, stakeholder equity, and resilience
Author(s) -
Marshall Curtis J.,
Roberts Blake,
Grenn Michael W.,
Holzer Thomas H.
Publication year - 2020
Publication title -
systems engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 50
eISSN - 1520-6858
pISSN - 1098-1241
DOI - 10.1002/sys.21552
Subject(s) - critical infrastructure , resilience (materials science) , automation , equity (law) , stakeholder , context (archaeology) , process management , business , computer science , knowledge management , risk analysis (engineering) , engineering , computer security , economics , political science , management , geography , mechanical engineering , physics , archaeology , law , thermodynamics
There is an urgent need for more efficient and resilient infrastructure systems to support a growing population with increasingly scarce resources worldwide. As the demand for limited natural and man‐made resources grows, improved methods for resolving anticipated and unforeseen conflicts of availability are needed. System automation has broadly been adopted for efficiency optimization and resource deconfliction via preplanned actions and responses to anticipated needs. However, human intervention is still relied upon up to resolve emergent issues for which automation lacks the flexibility and adaptability to resolve. In this research, a context‐based decision model for system automation is presented that uses satisficing heuristics to deconflict shared resources without preplanning or human intervention. Via modeling and simulation, the presented model and existing rule‐based algorithms were applied to an air traffic management problem to compare performance with respect to: (a) system efficiency, (b) standard deviation of efficiency, and (c) system stability as a measure of disruption avoidance. Simulation results demonstrate that the presented model concurrently supports efficiency optimization and disruption avoidance for airspace deconfliction, averaging 9.7% higher system efficiency and a 1.5% lower standard deviation of efficiency than the existing rule‐based standard for aircraft collision avoidance (based on an air traffic density of 45 aircraft per 10 000 square nautical miles). The model presented here is extensible to engineered systems that rely on shared, finite resources for mission execution. This research is relevant to the domains of critical infrastructure management, risk management, distributed control systems, and mission assurance.