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.

Ensuring pedestrian safety at high-risk, high-volume locations, such as hospitals, schools, stadiums, etc., requires consideration of the observed and possible future trends at access points of the transportation network. Existing performance metrics are often spatially and temporally aggregated, limiting their usefulness in assessing safety risks for time periods and locations of interest. Latest connected-vehicle (CV) technologies have improved both the volumes and resolutions of vehicle-movement data, with telemetry reported every few seconds. This study utilizes CV data in a multi-criteria analysis (MCA) to prioritize infrastructure improvements that improve pedestrian safety in school zones. The methods are demonstrated to update priorities for thirty-two candidate improvements at a single school vulnerable to evolving traffic and other conditions. Six performance criteria are addressed, including four criteria informed by CV event observations. The results highlight scenarios, articulated by the day of week and hour of the day, that are most disruptive to the priorities for improvements. The approach has interest across domains of systems engineering where trends and critical incidents of environment, markets, regulations, wear and tear, demographics, obsolescence, workforce etc. should influence systems evaluation and requirements.

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.

The resilience of vulnerable populations to environmental extremes is a concern for policymaking across environmental justice, economic development, technology innovation, etc. This study models the resilience of system order for a portfolio of investments, focusing on the spatial distributions of environmental stressors (i.e., precipitation, temperature, soil moisture, and elevation), social vulnerability, and risk exposure. The methodology quantifies risk as a disruption of baseline order under each of several scenarios that combine social and environmental factors, with attention to vulnerable populations. A realistic example is described with features of a southeastern region of the USA. The results and methodology are a rationale for the allocation of investments for economic development and system resilience, balancing among several criteria of social vulnerability and environmental justice.

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.

Blockchain, as an emerging decentralized architecture and distributed computing paradigm underlying Bitcoin and other cryptocurrencies, has attracted intensive attention in both research and applications recently. Blockchain, especially powered by chain-coded smart contracts, has the full potential of revolutionizing increasingly centralized cyber-physical-social systems (CPSSs) for constructions and applications, and reshaping traditional knowledge automation workflows. The key advantage of blockchain technology lies in the fact that it can enable the establishment of secured, trusted, and decentralized autonomous ecosystems for various scenarios, especially for better usage of the legacy devices, infrastructure, and resources.

Large, sudden influxes of individuals represent critical resource stressors to the food, energy, and water (FEW) systems that provide critical services to the region in which they serve. This paper describes progress in identification and monitoring of emergent and future conditions for FEW interdependent infrastructures of coastal cities. The approach seeks to identify combinations of conditions that are most and least disruptive to investments, assets, policies, locations, organizations, etc. The philosophy and methods build on the latest Systems Engineering Body of Knowledge of IEEE, INCOSE, et al. The effort should be of interest to systems engineers, researchers, and policy makers regarding principles, methods, and factors to consider to enhance FEW system resilience, reduce the impacts of population dislocation, and otherwise explore science and technology innovations for FEW infrastructures.

Coordinated mobility of people, goods, and energy is a foremost concern of infrastructure development. For example, deployment of advanced chargers and the associated communications networks will enable electric-vehicle fleets to serve particular needs of the power grid and microgrids. Incorporating plug-in electric vehicles as agents of energy storage in the grid can provide services to ancillary markets including frequency regulation. To prioritize research and development (R&D), this paper quantifies the sensitivity of vehicle-to-grid enterprise initiatives and milestones subject to a variety of emergent and future conditions, and demonstrates how the results of the analysis can be used for further investigation of initiatives. These conditions involve technology innovation (fast bi-directional chargers and related information technologies), environment, market prices, regulations, organizations, behaviors, workforce, etc. The results identify combinations of conditions for which research and development (R&D) are most beneficial. The approach includes scenario-based preferences to inform decision makers which initiatives are robust to variety of emergent and future conditions, thus supporting resilience of plans for electric vehicle fleets in logistics systems. An R&D initiative of particular concern is regional resource planning. An agent-based simulation is performed to forecast the availabilities of the vehicles at hour-long intervals, including route locations, and the state of charge of batteries. The paper has a context in the systems engineering body of knowledge (SEBoK), particularly as methodology for risk analysis and systems engineering.

The agencies responsible for transportation corridors tend to hold large volumes of data that can be relevant for both operations and planning. Meanwhile, it is a challenge for these agencies to prioritize their investments addressing risk, benefits, and costs. The agencies recognize an opportunity to improve project selection and programming with a centralized database of performance measures that will aid a consistent application of evaluation metrics. To support the identification, planning, and selection of highway transportation projects, this research has used a data structure known as dynamic segmentation for cross-referencing heterogeneous data sources including projects, traffic, safety, bridge and pavement conditions, etc. The result is a multiscale method for agencies to utilize big-data analytics and multicriteria decision analysis to assemble evidence for project selection and prioritization. The paper describes an approach to (1) visualize multiple attributes along linearized road corridors to identify future projects, minimize conflicts with current projects, and identify potential project synergies and (2) prioritize projects to the Top-20 with multiple performance factors. The methods are demonstrated at several geographic scales. The effort supports a fast, repeatable, and evidence-driven prioritization of projects and provides a complement to the use of electronic map data that has been overwhelming the available computing resources.

Uncertainties of climate change and other nonprobabilistic and structural uncertainties need to be addressed in strategic planning and priority setting for infrastructure systems. Traditional economic analysis and risk analysis of particular uncertainties can be prohibitive due to sparse data, complex models, and unforeseen interactions of climate change with other stressors. Nevertheless, planners need to proceed in the near term and may be asked to allocate resources to these deep uncertainties. This paper identifies and quantifies the influence of climate change combining with other sources of uncertainty to the priority order of projects in a portfolio of infrastructure investments. A demonstration for the Hampton Roads region of Virginia proceeds as follows. First, we apply traditional multicriteria analysis to generate a baseline prioritization of over 93 transportation projects. Next, we identify the following scenarios: climate conditions combined with economic conditions, wear and tear, ecological conditions, and traffic-demand conditions, and climate conditions alone. Next, we adjust a multicriteria value function for each scenario. We then quantify the sensitivity of the priority order of projects to the scenarios. Last, we identify the scenarios that are disruptive to the baseline prioritization. This approach is widely applicable to strategic planning for infrastructure systems that are subject to uncertainties of emergent and future conditions.

There is increasing need for agencies to coordinate their interdependent risk assessment, risk management, and risk communication activities in compliance with risk program guidelines. In particular, there is a challenge to measure risk program compliance and maturity to guidelines such as the U.S. Office of Management and Budget (OMB) memorandum “Updated Principles for Risk Analysis” among others. This paper demonstrates a systemic approach to evaluate large-scale risk program maturity with utilization of business process modeling and self-assessment methods. This approach will be helpful to agencies implementing risk guidelines such as those of the OMB, the U.S. Government Accountability Office, the U.S. Department of Homeland Security, the U.S. Department of Defense, and others. This paper will be of interest to risk managers, agencies, and risk and safety analysts engaged in the conception, implementation, and evaluation of risk and safety programs.

This paper develops a methodology for eliciting shifts in preference across future scenarios in the performance assessment of infrastructure policies and investments. The methodology quantifies the robustness of alternative portfolios across a variety of scenarios and identifies the scenarios that greatly affect the assessments. An innovation of the methodology is to elicit, for each scenario, only a few relative increases or decreases in importance of selected terms of the value function, which is more efficient than a full elicitation of the value function for each scenario. The identification of critical scenarios via our methodology can be used to focus resource-intensive and potentially costly modeling activities. The methodology integrates preference orders, centroid weights, and the Borda method. In a demonstration, the methodology assesses the relative sea level and other climate-change scenarios that could affect the performance of coastal protections.

The process of developing new information systems has evolved from custom software development to assembly of off-the-shelf components. The change has significantly reduced both the costs and time to develop new capabilities, and as a notable result, e-business systems have been implemented at a very rapid pace. An assembly sequence (components to be assembled, corresponding dates and costs) has several risks including: 1) technical risk: successful (or not) function of assembled components by planned schedule milestones; 2) operational risk: achieving (or not) the desired business value by using the new system of assembled components; and 3) programmatic (schedule and cost) risks: accomplishing the assembly within time and budget constraints. As assembly proceeds, estimates of technical performance and operational value at the time of system completion can be adjusted, and one should consider what early milestones of component assembly suggest about later milestones. The technical community can be both hesitant to reveal and ascertain the results of combining off-the-shelf products into a working system, and it is typical to have significant cost and schedule overruns due to technical problems that are discovered late in system assembly. The operational community can be surprised by the results achieved in applying new capabilities, causing significant changes to what was originally desired from a new system. This paper presents a framework for planning and adjusting milestone sequences in assembling off-the-shelf software components. The framework balances technical and operational risks within established cost and time constraints.

The intelligence community must provide relevant, timely intelligence to support operations other than war (OOTW). Recently, OOTW have become an increasing challenge to military planners. The intelligence community does not know with certainty where or when future OOTW will occur, what the operations will involve, when they will occur, or how much advance warning will be provided. To prudently deal with these challenges, the US Army's National Ground Intelligence Center (NGIC) has developed a plan to systematically identify knowledge needs to support future OOTW. We present our preliminary knowledge hierarchy for the collection of intelligence information to help national security planners prepare for future OOTW. We developed a prototype to accept user input on alternative deployment locations and value assessments for criteria from the knowledge hierarchy to determine the best location. For a specific location, criteria receiving a score below a threshold generate a needed capability for the deploying unit. By analyzing locations that could serve as military areas of operations, the prototype answers the questions as to where to deploy and what to bring. This system represents the first attempt to use intelligence information in a decision support system that specifically addresses the needs of OOTW planners at multiple levels of command.

We use the theory of order statistics, the concepts of first- and second-order stochastic dominance (FSD and SSD) to develop an order statistics SSD minimax decision rule. It can be used to refine choice within the random variables in the SSD noninferior set. We are able to reduce the size of the SSD noninferior set when we assume that the decision-maker is most concerned about the potential adverse outcomes at the right tail of the probability distribution. In other words, we consider the risk of extreme events and build on order statistics in order to refine the decision rules. In some eases, the order statistics SSD minimax decision rule can provide us with a unique choice from among the SSD noninferior set. We define the concept of conditional second-order stochastic dominance (CSSD) in order to model the risk of extreme events. We also use the concept of CSSD to develop a CSSD minimax decision rule.

We investigate the second-order stochastic ordering of the extreme value distributions within each of three families: the Gumbel distribution, the Frechet distribution, and the Weibull distribution. We give conditions for second-order stochastic dominance, conditional second-order stochastic dominance, and order statistics second-order stochastic dominance within the three families.