Infrastructure Asset Management System Optimized Configuration: A Genetic Algorithm–Complex Network Theoretic Metamanagement Approach

Abstract

An effective infrastructure asset management (AM) system is crucial for utilities, city officers, government agencies, and other asset-owning organizations to facilitate navigating the numerous challenges associated with operating and managing infrastructure assets. In this paper, the AM system itself is represented as a complex network (comprised of nodes and links) that describes the major components necessary for its operation within an organization and the information connections between such components. ISO 55001, a widely accepted international standard, specifies the requirements for an effective AM system and outlines the criticality levels of different system components-reflected in the corresponding network by the link weights. The main challenges facing managing an AM system (i.e., metamanagement) pertain to (1) information asymmetry (i.e., not relying on consistent information for decision making) between AM system components; and (2) information overload (i.e., excessive information undermining decision making) within the AM system components. These challenges cause systemic risks (possibility of dependence-induced disruptions) within the system network due to the connectedness of system components. Systemic risks can be mitigated through built-in network resilience by restructuring the system component connections. Such network reconfiguration presents a complex nonconvex optimization problem with multiple potential solutions depending on the number of new connections to be added to the AM system, the length of those connections, and the target risk mitigation level. Through this metamanagement (managing the management system) lens, a genetic algorithm approach was employed to explore the optimal AM network configurations considering different objective functions. These objective functions were based on different complex network measures including the betweenness-, closeness-, and eigenvector-centrality, as well as the vulnerability index. The devised objective functions were employed to the cases of adding 1 to 15 links only to limit network overconnectedness (i.e., information overload). Considering all objective functions evaluated, adding a small number of links (fewer than five) provided a significant reduction in systemic risk (18% to 49%). Finally, managerial insights are presented to explain how to employ the developed approach to mitigate the systemic risks within an organization's AM system based on different metrics valuations and stakeholder inputs.

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