Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows

Given the ubiquitous nature of infrastructure networks in today's society, there is a global need to understand, quantify, and plan for the resilience of these networks to disruptions. This work defines network resilience along dimensions of reliability, vulnerability, survivability, and recoverability, and quantifies network resilience as a function of component and network performance. The treatment of vulnerability and recoverability as random variables leads to stochastic measures of resilience, including time to total system restoration, time to full system service resilience, and time to a specific α% resilience. Ultimately, a means to optimize network resilience strategies is discussed, primarily through an adaption of the Copeland Score for nonparametric stochastic ranking. The measures of resilience and optimization techniques are applied to inland waterway networks, an important mode in the larger multimodal transportation network upon which we rely for the flow of commodities. We provide a case study analyzing and planning for the resilience of commodity flows along the Mississippi River Navigation System to illustrate the usefulness of the proposed metrics.     

Spatial Risk Analysis of Power Systems Resilience During Extreme Events

The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real‐time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience‐based decisions for risk mitigation. The IEEE 24‐bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method.     

On the Complex Quantification of Risk: Systems‐Based Perspective on Terrorism

This article highlights the complexity of the quantification of the multidimensional risk function, develops five systems‐based premises on quantifying the risk of terrorism to a threatened system, and advocates the quantification of vulnerability and resilience through the states of the system. The five premises are: (i) There exists interdependence between a specific threat to a system by terrorist networks and the states of the targeted system, as represented through the system's vulnerability, resilience, and criticality‐impact. (ii) A specific threat, its probability, its timing, the states of the targeted system, and the probability of consequences can be interdependent. (iii) The two questions in the risk assessment process: “What is the likelihood?” and “What are the consequences?” can be interdependent. (iv) Risk management policy options can reduce both the likelihood of a threat to a targeted system and the associated likelihood of consequences by changing the states (including both vulnerability and resilience) of the system. (v) The quantification of risk to a vulnerable system from a specific threat must be built on a systemic and repeatable modeling process, by recognizing that the states of the system constitute an essential step to construct quantitative metrics of the consequences based on intelligence gathering, expert evidence, and other qualitative information. The fact that the states of all systems are functions of time (among other variables) makes the time frame pivotal in each component of the process of risk assessment, management, and communication. Thus, risk to a system, caused by an initiating event (e.g., a threat) is a multidimensional function of the specific threat, its probability and time frame, the states of the system (representing vulnerability and resilience), and the probabilistic multidimensional consequences.     

Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System

Coupled infrastructure systems and complicated multihazards result in a high level of complexity and make it difficult to assess and improve the infrastructure system resilience. With a case study of the Greater Toronto Area energy system (including electric, gas, and oil transmission networks), an approach to analysis of multihazard resilience of an interdependent infrastructure system is presented in the article. Integrating network theory, spatial and numerical analysis methods, the new approach deals with the complicated multihazard relations and complex infrastructure interdependencies as spatiotemporal impacts on infrastructure systems in order to assess the dynamic system resilience. The results confirm that the effects of sequential hazards on resilience of infrastructure (network) are more complicated than the sum of single hazards. The resilience depends on the magnitude of the hazards, their spatiotemporal relationship and dynamic combined impacts, and infrastructure interdependencies. The article presents a comparison between physical and functional resilience of an electric transmission network, and finds functional resilience is always higher than physical resilience. The multiple hazards resilience evaluation approach is applicable to any type of infrastructure and hazard and it can contribute to the improvement of infrastructure planning, design, and maintenance decision making.     

破解颠覆性技术创新的跨界网络治理路径——基于百度Apollo自动驾驶开放平台的案例研究

本文以百度Apollo自动驾驶开放平台为案例研究对象,研究了平台以跨界网络治理驱动颠覆性技术创新的演进过程及其内在机制,研究发现:(1)跨界平台网络是不同于单个企业的新型技术创新组织载体,可以通过技术网络丰富、市场网络丰富和两个网络价值共创的继起性演进在驱动颠覆性技术创新中发挥重要作用;(2)在跨界网络驱动颠覆性技术创新的不同时期,为解决技术与市场不确定性高以及网络参与者关系复杂等难题,平台可以通过网络治理路径的选择促进颠覆性创新的不同过程与结果,在技术网络丰富期,平台可以通过内容治理与关系治理促进技术的内容颠覆与结构颠覆;在市场网络丰富期,可以通过价值治理与关系治理促进市场的价值颠覆与结构颠覆;在技术网络与市场网络协同的价值共创期,可以通过"网络平台包络(内容治理)、跨界网络效应激发(关系治理)、异质性利润焦点获取(价值治理)"的综合治理促进技术与市场的颠覆可持续性;(3)平台治理中的内容治理与价值治理是跨界网络治理的"硬实力",而关系治理是跨界网络治理的"软实力",软硬实力的组合共同促进颠覆性技术的技术突破与市场突破。本文丰富了网络治理理论、颠覆性创新理论和平台理论,并对尝试借助平台推进新兴前沿技术突破、尤其是跨界推进颠覆性技术创新的实践行动具有管理启示。     

On the Complex Definition of Risk: A Systems-Based Approach

The premise of this article is that risk to a system, as well as its vulnerability and resilience, can be understood, defined, and quantified most effectively through a systems-based philosophical and methodological approach, and by recognizing the central role of the system states in this process. A universally agreed-upon definition of risk has been difficult to develop; one reason is that the concept is multidimensional and nuanced. It requires an understanding that risk to a system is inherently and fundamentally a function of the initiating event, the states of the system and of its environment, and the time frame. In defining risk, this article posits that: (a) the performance capabilities of a system are a function of its state vector; (b) a system's vulnerability and resilience vectors are each a function of the input (e.g., initiating event), its time of occurrence, and the states of the system; (c) the consequences are a function of the specificity and time of the event, the vector of the states, the vulnerability, and the resilience of the system; (d) the states of a system are time-dependent and commonly fraught with variability uncertainties and knowledge uncertainties; and (e) risk is a measure of the probability and severity of consequences. The above implies that modeling must evaluate consequences for each risk scenario as functions of the threat (initiating event), the vulnerability and resilience of the system, and the time of the event. This fundamentally complex modeling and analysis process cannot be performed correctly and effectively without relying on the states of the system being studied.     

Resilience and Climate Change Adaptation: The Importance of Framing

In the Australian policy context, there has recently been a discernible shift in the discourse used when considering responses to the impacts of current weather extremes and future climate change. Commonly used terminology, such as climate change impacts and vulnerability, is now being increasingly replaced by a preference for language with more positive connotations as represented by resilience and a focus on the ‘strengthening’ of local communities. However, although this contemporary shift in emphasis has largely political roots, the scientific conceptual underpinning for resilience, and its relationship with climate change action, remains contested. To contribute to this debate, the authors argue that how adaptation is framed—in this case by the notion of resilience—can have an important influence on agenda setting, on the subsequent adaptation pathways that are pursued and on eventual adaptation outcomes. Drawing from multi-disciplinary adaptation research carried out in three urban case studies in the State of Victoria, Australia (‘Framing multi-level and multi-actor adaptation responses in the Victorian context’, funded by the Victorian Centre for Climate Change Adaptation Research (2010–2012)), this article is structured according to three main discussion points. Firstly, the importance of being explicit when framing adaptation; secondly, this study reflects on how resilience is emerging as part of adaptation discourse and narratives in different scientific, research and policy-making communities; and finally, the authors reflect on the implications of resilience framing for evolving adaptation policy and practice.     

Probabilistic Multiple Hazard Resilience Model of an Interdependent Infrastructure System

Multiple hazard resilience is of significant practical value because most regions of the world are subject to multiple natural and technological hazards. An analysis and assessment approach for multiple hazard spatiotemporal resilience of interdependent infrastructure systems is developed using network theory and a numerical analysis. First, we define multiple hazard resilience and present a quantitative probabilistic metric based on the expansion of a single hazard deterministic resilience model. Second, we define a multiple hazard relationship analysis model with a focus on the impact of hazards on an infrastructure. Subsequently, a relationship matrix is constructed with temporal and spatial dimensions. Further, a general method for the evaluation of direct impacts on an individual infrastructure under multiple hazards is proposed. Third, we present an analysis of indirect multiple hazard impacts on interdependent infrastructures and a joint restoration model of an infrastructure system. Finally, a simplified two‐layer interdependent infrastructure network is used as a case study for illustrating the proposed methodology. The results show that temporal and spatial relationships of multiple hazards significantly influence system resilience. Moreover, the interdependence among infrastructures further magnifies the impact on resilience value. The main contribution of the article is a new multiple hazard resilience evaluation approach that is capable of integrating the impacts of multiple hazard interactions, interdependence of network components (layers), and restoration strategy.     

Selecting Indicators for Assessing Community Sustainable Resilience

Communities are complex systems subject to a variety of hazards that can result in significant disruption to critical functions. Community resilience assessment is rapidly gaining popularity as a means to help communities better prepare for, respond to, and recover from disruption. Sustainable resilience, a recently developed concept, requires communities to assess system‐wide capability to maintain desired performance levels while simultaneously evaluating impacts to resilience due to changes in hazards and vulnerability over extended periods of time. To enable assessment of community sustainable resilience, we review current literature, consolidate available indicators and metrics, and develop a classification scheme and organizational structure to aid in identification, selection, and application of indicators within a dynamic assessment framework. A nonduplicative set of community sustainable resilience indicators and metrics is provided that can be tailored to a community's needs, thereby enhancing the ability to operationalize the assessment process.     

Workforce/Population,Economy, Infrastructure,Geography, Hierarchy,and Time (WEIGHT): Reflections on the Plural Dimensions of Disaster Resilience

The concept of resilience and its relevance to disaster risk management has increasingly gained attention in recent years. It is common for risk and resilience studies to model system recovery by analyzing a single or aggregated measure of performance, such as economic output or system functionality. However, the history of past disasters and recent risk literature suggest that a single-dimension view of relevant systems is not only insufficient, but can compromise the ability to manage risk for these systems. In this article, we explore how multiple dimensions influence the ability for complex systems to function and effectively recover after a disaster. In particular, we compile evidence from the many competing resilience perspectives to identify the most critical resilience dimensions across several academic disciplines, applications, and disaster events. The findings demonstrate the need for a conceptual framework that decomposes resilience into six primary dimensions: workforce/population, economy, infrastructure, geography, hierarchy, and time (WEIGHT). These dimensions are not typically addressed holistically in the literature; often they are either modeled independently or in piecemeal combinations. The current research is the first to provide a comprehensive discussion of each resilience dimension and discuss how these dimensions can be integrated into a cohesive framework, suggesting that no single dimension is sufficient for a holistic analysis of a disaster risk management. Through this article, we also aim to spark discussions among researchers and policymakers to develop a multicriteria decision framework for evaluating the efficacy of resilience strategies. Furthermore, the WEIGHT dimensions may also be used to motivate the generation of new approaches for data analytics of resilience-related knowledge bases.     

Validating Resilience and Vulnerability Indices in the Context of Natural Disasters

Due to persistent and serious threats from natural disasters around the globe, many have turned to resilience and vulnerability research to guide disaster preparation, recovery, and adaptation decisions. In response, scholars and practitioners have put forth a variety of disaster indices, based on quantifiable metrics, to gauge levels of resilience and vulnerability. However, few indices are empirically validated using observed disaster impacts and, as a result, it is often unclear which index should be preferred for each decision at hand. Thus, we compare and empirically validate five of the top U.S. disaster indices, including three resilience indices and two vulnerability indices. We use observed disaster losses, fatalities, and disaster declarations from the southeastern United States to empirically validate each index. We find that disaster indices, though thoughtfully substantiated by literature and theoretically persuasive, are not all created equal. While four of the five indices perform as predicted in explaining damages, only three explain fatalities and only two explain disaster declarations as expected by theory. These results highlight the need for disaster indices to clearly state index objectives and structure underlying metrics to support validation of the results based on these goals. Further, policymakers should use index results carefully when developing regional policy or investing in resilience and vulnerability improvement projects.     

Murphy Scale: A locational equivalent intensity scale for hazard events

Empirical cross-hazard analysis and prediction of disaster vulnerability, resilience, and risk requires a common metric of hazard strengths across hazard types. In this paper, the authors propose an equivalent intensity scale for cross-hazard evaluation of hazard strengths of events for entire durations at locations. The proposed scale is called the Murphy Scale, after Professor Colleen Murphy. A systematic review and typology of hazard strength metrics is presented to facilitate the delineation of the defining dimensions of the proposed scale. An empirical methodology is introduced to derive equivalent intensities of hazard events on a Murphy Scale. Using historical data on impacts and hazard strength indicators of events from 2013 to 2017, the authors demonstrate the utility of the proposed methodology for computing the equivalent intensities for earthquakes and tropical cyclones. As part of a new area of research called hazard equivalency, the proposed Murphy Scale paves the way toward creating multi-hazard hazard maps. The proposed scale can also be leveraged to facilitate hazard communication regarding past and future local experiences of hazard events for enhancing multi-hazard preparedness, mitigation, and emergency response.     

An effective algorithm for obtaining the whole set of minimal cost pairs of disjoint paths with dual arc costs

In telecommunication networks design the problem of obtaining optimal (arc or node) disjoint paths, for increasing network reliability, is extremely important. The problem of calculating k _c disjoint paths from s to t (two distinct nodes), in a network with k _c different (arbitrary) costs on every arc such that the total cost of the paths is minimised, is NP-complete even for k _c =2. When k _c =2 these networks are usually designated as dual arc cost networks.     

Beyond the Matrix: Visual Methods for Qualitative Network Research

Network research remains dominated by approaches involving the analysis of numerical data stored in data matrices with the aim of identifying the effects of hidden social structures. While such research has advanced our understanding of social networks at the inter‐personal, inter‐unit and inter‐organizational level, repeated calls have been made for network research to attend to the situated meanings attached to both relationships and network structures. In this article, we advance a nascent literature on qualitative methods for social network analysis by drawing together developments in visual network research from across the social sciences. We introduce a typology of three visual methods for the collection of network data using network maps: participatory network mapping, network map interviews and visual network surveys. Drawing on three empirical examples from our research in the inter‐organizational domain, we demonstrate how these methods can be used for the collection of qualitative and quantitative relational data, and how they can be triangulated with other qualitative methods and social network analysis. We evaluate the merits and limitations of the methods presented and conclude that visual network research is a useful addition to existing methods for network research in business and management studies.     

Risk‐Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

According to Codex Alimentarius Commission recommendations, management options applied at the process production level should be based on good hygiene practices, HACCP system, and new risk management metrics such as the food safety objective. To follow this last recommendation, the use of quantitative microbiological risk assessment is an appealing approach to link new risk‐based metrics to management options that may be applied by food operators. Through a specific case study, Listeria monocytogenes in soft cheese made from pasteurized milk, the objective of the present article is to practically show how quantitative risk assessment could be used to direct potential intervention strategies at different food processing steps. Based on many assumptions, the model developed estimates the risk of listeriosis at the moment of consumption taking into account the entire manufacturing process and potential sources of contamination. From pasteurization to consumption, the amplification of a primo‐contamination event of the milk, the fresh cheese or the process environment is simulated, over time, space, and between products, accounting for the impact of management options, such as hygienic operations and sampling plans. A sensitivity analysis of the model will help orientating data to be collected prioritarily for the improvement and the validation of the model. What‐if scenarios were simulated and allowed for the identification of major parameters contributing to the risk of listeriosis and the optimization of preventive and corrective measures.     

Informing Ex Ante Event Studies with Macro‐Econometric Evidence on the Structural and Policy Impacts of Terrorism

Economic consequence analysis is one of many inputs to terrorism contingency planning. Computable general equilibrium (CGE) models are being used more frequently in these analyses, in part because of their capacity to accommodate high levels of event‐specific detail. In modeling the potential economic effects of a hypothetical terrorist event, two broad sets of shocks are required: (1) physical impacts on observable variables (e.g., asset damage); (2) behavioral impacts on unobservable variables (e.g., investor uncertainty). Assembling shocks describing the physical impacts of a terrorist incident is relatively straightforward, since estimates are either readily available or plausibly inferred. However, assembling shocks describing behavioral impacts is more difficult. Values for behavioral variables (e.g., required rates of return) are typically inferred or estimated by indirect means. Generally, this has been achieved via reference to extraneous literature or ex ante surveys. This article explores a new method. We elucidate the magnitude of CGE‐relevant structural shifts implicit in econometric evidence on terrorist incidents, with a view to informing future ex ante event assessments. Ex post econometric studies of terrorism by Blomberg et al . yield macro econometric equations that describe the response of observable economic variables (e.g., GDP growth) to terrorist incidents. We use these equations to determine estimates for relevant (unobservable) structural and policy variables impacted by terrorist incidents, using a CGE model of the United States. This allows us to: (i) compare values for these shifts with input assumptions in earlier ex ante CGE studies; and (ii) discuss how future ex ante studies can be informed by our analysis.     

Interventions for musculoskeletal disorders in computer-intense office work: A framework for evaluation

Workplace interventions for work-related musculoskeletal disorders (WMSDs) are usually multifaceted. These interventions tend to deal with multiple work organizational and physical risk factors and have components occurring at different organizational levels. The organizations are often changing, with shifting initiatives and priorities. Evaluation of such interventions poses challenges in documentation of implementation, reduction in exposures, and assessment of improved health outcomes. We describe a means-outcomes framework for evaluating field interventions that includes strategies, activities, objectives and metrics for outcomes using quantitative and qualitative methods. We demonstrate application of the framework to our work with a large newspaper, which builds on existing laboratory, aetiological and best practice evidence to improve musculoskeletal health. The newspaper had adopted several organizational strategies aimed at improving financial performance, including restructuring into teams, a major set of moves/renovations and introduction of new software. Concomitant WMSD-related organizational strategies include an ergonomics policy, human resources activities, promotion of team work, changes in environment and equipment specifications and development of information systems. We have found the framework useful for focusing the purpose of data collection and ensuring coverage of important components. At the same time, it provides sufficient flexibility to respond to the changing process of implementation.     

Selecting human health metrics for environmental decision-support tools.

Environmental decision‐support tools often predict a multitude of different human health effects due to environmental stressors. The accounting and aggregating of these morbidity and mortality outcomes is key to support decision making and can be accomplished by different methods that we call human health metrics. This article attempts to answer two questions: Does it matter which metric is chosen? and What are the relevant characteristics of these metrics in environmental applications? Three metrics (quality adjusted life years (QALYs), disability adjusted life years (DALYs), and willingness to pay (WTP)) have been applied to the same diverse set of health effects due to environmental impacts. In this example, the choice of metric mattered for the ranking of these environmental impacts and it was found for this example that WTP was dominated by mortality outcomes. Further, QALYs and DALYs are sensitive to mild illnesses that affect large numbers of people and the severity of these mild illnesses are difficult to assess. Eight guiding questions are provided in order to help select human health metrics for environmental decision‐support tools. Since health metrics tend to follow the paradigm of utility maximization, these metrics may be supplemented with a semi‐quantitative discussion of distributional and ethical aspects. Finally, the magnitude of age‐dependent disutility due to mortality for both monetary and nonmonetary metrics may bear the largest practical relevance for future research.     

Performance Metric Portfolios: A Framework and Empirical Analysis

The widespread adoption of supply chain management principles suggests that managers recognize the importance of evaluating operational decisions holistically. However, it is often difficult to link specific operational practices to strategic level outcomes and in turn to corporate financial results. This presents problems for both managers and academic researchers attempting to justify the often high cost of operational improvement initiatives in terms of objective accounting metrics. This study provides evidence that it is possible to demonstrate linkages between carefully chosen portfolios of tactical, strategic, and financial metrics. Survey data from 118 manufacturers are used to evaluate hypotheses linking multilevel metrics of performance across three well‐established strategic foci. We present portfolios of metrics drawn from the literature and from the Supply Chain Counciľs supply‐chain operations reference model and related design and customer chain models. Our analysis suggests that metric portfolios in which tactical metrics are designed to match strategic‐level metrics, based on alignment with a specific strategic focus, provide clearer mechanisms for understanding performance linkages.     

Designing <Emphasis Type="Italic">k</Emphasis>-coverage schedules in wireless sensor networks

Some sensor network applications require k-coverage to ensure the quality of surveillance. Meanwhile, energy is another primary concern for sensor networks. In this paper, we investigate the Sensor Scheduling for k-Coverage (SSC) problem which requires to efficiently schedule the sensors, such that the monitored area can be k-covered throughout the whole network lifetime with the purpose of maximizing network lifetime. The SSC problem is NP-hard and we propose two heuristic algorithms under different scenarios. In addition, we develop a guideline for users to better design a sensor deployment plan to save energy by employing a density control scheme. Simulation results are presented to evaluate our proposed algorithms.