There is urgency for control and decision technologies to address the threat of wildfires as they endanger built and natural systems and lead to multifaceted consequences for ecosystem, human, societal, and economic well-being. Reliable early detection of new wildfires aids in reducing the impacts of this planetary emergency by enabling responses while fires remain small. With the development of new detection technologies, the variety of available technologies calls for a systems-level framework to effectively analyze the prioritization of technologies and sources of disruption to this prioritization. This paper applies a scenario-based multi-criteria decision analysis approach to assess priorities in detection and monitoring technologies under baseline and potentially disruptive scenarios. Sixteen categories of wildfire detection and monitoring technologies are evaluated under seven scenarios, including sociotechnical considerations, using nine success criteria.

Control and decision making of water supply systems influence economic, political, and social variables on several time horizons. Particularly in Central Asia, the international nature of water resources, large irrigation requirements, and depletion of surface water and aquifers in the arid region call for a systems-level framework to analyze disruptive scenarios and determine a schedule of risk countermeasures. This paper evaluates risk as the influence of scenarios on system order. Through scenario-based multi-criteria decision analysis, system initiatives are prioritized in the baseline and disruptive scenarios. The methods are applied to a case of water policies in Turkmenistan where twelve water policies are ordered according to six system criteria including social, situational, and economic factors. The system order is updated across each of seven disruptive scenarios relating to political, economic, technological, and societal trends and forecasts.

Disruption of complex infrastructures systems involves cascading failures and interdependencies. This paper presents a network-based approach to assessing infrastructure resilience using scenario-based disruptions that remove entire sectors from the network. This approach evaluates system-wide vulnerabilities by modeling structural failures through the removal of nodes from the infrastructure graph. The framework uses a directed graph to represent interdependencies and uses eigenvector centrality to rank sector influence. Disruptive scenarios, including power outages, communication failures, and hybrid threats are applied to evaluate changes in system order. Spearman’s rank correlation quantifies the disruptiveness of each scenario, identifying which sectors experience the most significant shifts in importance. Results show that disruptions to the communications sector cause the greatest reordering of system orders, while disruptions to water & wastewater have a lower impact. The analysis demonstrates how different hazards affect regional resilience and provides insights for decision-makers to schedule the risk countermeasures.

Decision making for resilient infrastructure systems requires methods for assessing risk across political, social, economic, and physical domains. Traditional risk assessments for physical systems often consider only historical data in prioritization and decision making. This paper presents a systems and risk analysis framework to identify resource threats to energy system development priorities. The methods are demonstrated for the case of alternative energy investments in Turkmenistan. Twenty-seven sub-basins are ordered according to three satellite-observed metrics representing energy generation potential. Disruptions to the baseline system order, induced by 30-year forward-looking projection scenarios, are used to identify the most and least resilient sub-basins in the context of energy system development.

The security of electronics and embedded hardware assets is critical to the operations of industrial organizations and facilities. Sources of risk, such as counterfeit parts, can compromise operations and result in degraded functionality and other negative impacts. However, accounting for and prioritizing numerous electronic assets within a company is difficult. This paper describes a security audit process that facilitates tracking and management of risks through a scenario-based methodology. The process allows facility managers to account for impacts to organizational security objectives such as confidentiality, integrity, availability, and accountability. The scenarios that are the most disruptive to system order can be determined. The results guide the implementation of risk reduction countermeasures. The paper is widely relevant to the identification and tracking of security vulnerabilities for a variety of large-scale systems.

Iraq's infrastructure's water and environmental security are influenced by its semi-arid to arid climate, marked by erratic variations, including minimal precipitation, increasing air temperatures, and compromised water quality resulting from reduced inflow in the tributary. The transportation sector plays a vital role in improving economic conditions and mitigating the impacts of climate change. However, critical transportation systems, essential for the movement of goods, information, and people, are jeopardized by water scarcity and climate change. This study develops a sensitivity analysis of the priorities among transportation nodes subject to their disruption by water scarcity and other emergent and future stressors. The stressors include social, technological, regulatory, workforce, market, climate, and hydrologic criteria. This analysis describes the nodes of highest importance and quantifies which scenarios are the most and least disruptive to the system order of nodes. This study includes thirteen order criteria, forty-three nodes, and seven risk scenarios. The paper should interest the system owners and operators who are concerned with monitoring their enterprises' resilience and environmental security.

Global logistics systems and supply chains face disruptions to operations including demand fluctuations, natural and human-caused disasters, pandemics, market prices, technology innovations, regulations, etc. Maritime container ports are susceptible to the cascading effects of these disruptions. This paper explores the use of the MuZero reinforcement learning algorithm to manage the container stacking problem, and thus increase resilience of maritime container ports to disruptions. Where previous work focused on touches per container, this paper emphasizes distance traveled per container. This approach improves accuracy for energy usage and adds insight to dynamics of container stacking. The results provide port managers with essential understanding of container handling operations, including heuristics for improving efficiency and evaluating the performance gains from changing container storage. The paper has relevance for a variety of engineering systems seeking to improve enterprise resilience to disruptions.

Global logistics systems are in an unprecedented crisis from the pandemic, workforce disruptions, supply shortages, and demand surges. Shortages of goods and services, surges of demand, and an evolving workforce call for methods that address system resilience. Maritime ports in particular are vulnerable to these disruptions. The container stacking process is especially vulnerable, as it is a bottleneck in container management. This paper presents a simulation and reinforcement learning methodology for managing container stacking blocks. The container stacking problem is known to be difficult or impossible to optimize, with most ports using black box heuristic models. A simulation and reinforcement learning approach addresses these challenges. Simulation is an effective tool for and testing different inputs, parameter changes, noise, and changes to systems due to disruptive scenarios. Reinforcement learning is especially helpful for contexts in which traditional optimization is cost and computationally prohibitive, or where data is difficult to collect and analyze. This paper applies reinforcement learning with a mathematical simulation of the container stacking problem, achieving similar results to maritime ports. The results can be used to assess performance under disruptive scenarios, as well as to test new container storage configurations.