A Comprehensive Risk Analysis of Transportation Networks Affected by Rainfall‐Induced Multihazards

Climate change and its projected natural hazards have an adverse impact on the functionality and operation of transportation infrastructure systems. This study presents a comprehensive framework to analyze the risk to transportation infrastructure networks that are affected by natural hazards. The proposed risk analysis method considers both the failure probability of infrastructure components and the expected infrastructure network efficiency and capacity loss due to component failure. This comprehensive approach facilitates the identification of high‐risk network links in terms of not only their susceptibility to natural hazards but also their overall impact on the network. The Chinese national rail system and its exposure to rainfall‐related multihazards are used as a case study. The importance of various links is comprehensively assessed from the perspectives of topological, efficiency, and capacity criticality. Risk maps of the national railway system are generated, which can guide decisive action regarding investments in preventative and adaptive measures to reduce risk.     

Geographic Hotspots of Critical National Infrastructure

Failure of critical national infrastructures can result in major disruptions to society and the economy. Understanding the criticality of individual assets and the geographic areas in which they are located is essential for targeting investments to reduce risks and enhance system resilience. Within this study we provide new insights into the criticality of real‐life critical infrastructure networks by integrating high‐resolution data on infrastructure location, connectivity, interdependence, and usage. We propose a metric of infrastructure criticality in terms of the number of users who may be directly or indirectly disrupted by the failure of physically interdependent infrastructures. Kernel density estimation is used to integrate spatially discrete criticality values associated with individual infrastructure assets, producing a continuous surface from which statistically significant infrastructure criticality hotspots are identified. We develop a comprehensive and unique national‐scale demonstration for England and Wales that utilizes previously unavailable data from the energy, transport, water, waste, and digital communications sectors. The testing of 200,000 failure scenarios identifies that hotspots are typically located around the periphery of urban areas where there are large facilities upon which many users depend or where several critical infrastructures are concentrated in one location.     

Improbability of Igneous Intrusion Promoting a Critical Event in Spent Nuclear Fuel Disposed in Unsaturated Tuff

In their regulations, the U.S. Environmental Protection Agency and the U.S. Nuclear Regulatory Commission permit the omission of features, events, or processes with probabilities of <10(-4) in 10(4) yr (e.g., a constant frequency of <10(-8) per yr) in assessments of the performance of radioactive waste disposal systems. Igneous intrusion (or "volcanism") of a geologic repository at Yucca Mountain for radioactive waste is one disruptive event that has a probability with a range of uncertainty that straddles this regulatory criterion and is considered directly in performance assessment calculations. A self-sustained nuclear chain reaction (or "criticality") is another potentially disruptive event to consider, although it was never found to be important when evaluating the efficacy of radioactive waste disposal since the early 1970s. The thesis of this article is that the consideration of the joint event--volcanism and criticality--occurring in any 10,000-year period following closure can be eliminated from performance calculations at Yucca Mountain. The probability of the joint event must be less than the fairly well-accepted but low probability of volcanism. Furthermore, volcanism does not "remove" or "fail" existing hydrologic or geochemical constraints at Yucca Mountain that tend to prevent concentration of fissile material. Prior to general corrosion failure of waste packages, the mean release of fissile mass caused by a low-probability, igneous intrusive event is so small that the probability of a critical event is remote, even for highly enriched spent nuclear fuel owned by the U.S. Department of Energy. After widespread failure of packages occurs, the probability of the joint event is less than the probability of criticality because of the very small influence of volcanism on the mean fissile mass release. Hence, volcanism plays an insignificant role in inducing criticality over any 10(4)-yr period. We also argue that the Oklo reactors serve as a natural analogue and provide a rough bound on probability of criticality given favorable hydrologic or geochemical conditions on the scale of the repository that is less than 0.10. Because the product of this bound with the probability of volcanism represents the probability of the joint event and the product is less than 10(-4) in 10(4) yr, consideration of the joint event can be eliminated from performance calculations.     

Evaluation of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) and the Collected Data

To protect and secure food resources for the United States, it is crucial to have a method to compare food systems’ criticality. In 2007, the U.S. government funded development of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) to determine which food and agriculture systems were most critical to the nation. FASCAT was developed in a collaborative process involving government officials and food industry subject matter experts (SMEs). After development, data were collected using FASCAT to quantify threats, vulnerabilities, consequences, and the impacts on the United States from failure of evaluated food and agriculture systems. To examine FASCAT's utility, linear regression models were used to determine: (1) which groups of questions posed in FASCAT were better predictors of cumulative criticality scores; (2) whether the items included in FASCAT's criticality method or the smaller subset of FASCAT items included in DHS's risk analysis method predicted similar criticality scores. Akaike's information criterion was used to determine which regression models best described criticality, and a mixed linear model was used to shrink estimates of criticality for individual food and agriculture systems. The results indicated that: (1) some of the questions used in FASCAT strongly predicted food or agriculture system criticality; (2) the FASCAT criticality formula was a stronger predictor of criticality compared to the DHS risk formula; (3) the cumulative criticality formula predicted criticality more strongly than weighted criticality formula; and (4) the mixed linear regression model did not change the rank‐order of food and agriculture system criticality to a large degree.     

Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty

The design and planning of resilient supply chains is a major challenge due to the increasing complexity of these systems that operate in a global market and therefore are more exposed to disruptions. In the present work a design and planning model that integrates demand uncertainty is applied to five supply chain structures that are submitted to different types of disruptions. Disruptions are modelled in a probabilistic manner, resulting in the incorporation of two sources of uncertainty. Eleven indicators are considered to assess the supply chains’ resilience, which comprise network design, centralization and operational indicators. The goal is to provide managers what are expected operational impacts (measured by the operational indicators) by assessing the behavior of network and centralization indicators and their known resilience behaviors from the literature. A case study of a European supply chain is used to illustrate the methodology and a discussion on the results obtained is presented in order to conclude which main characteristics a manager should consider when designing and planning resilient supply chains.     

多标度条件下资本市场波动的级串方向和结构

对上证综指多标度条件下波动级串方向和结构的实证研究发现,在4days临界标度范围内,大小波动间互为因果;超出临界标度,大小波动间表现出单方向性特征.运用交叉相关系数和功率谱方法从相关性和能量耗散2个角度对多标度波动级串传递方向的分析同样显示,在101～103min的3个数量级广泛标度范围内,大波动对于小波动具有较强预测能力,波动级串结构表现出大波动影响小波动变化的单方向特征.在此基础上,提出了4类"递进"的资本市场波动级串模型及其运用于风险管理的基本思路.     

Optimization of Cascade‐Resilient Electrical Infrastructures and its Validation by Power Flow Modeling

Large‐scale outages on real‐world critical infrastructures, although infrequent, are increasingly disastrous to our society. In this article, we are primarily concerned with power transmission networks and we consider the problem of allocation of generation to distributors by rewiring links under the objectives of maximizing network resilience to cascading failure and minimizing investment costs. The combinatorial multiobjective optimization is carried out by a nondominated sorting binary differential evolution (NSBDE) algorithm. For each generators–distributors connection pattern considered in the NSBDE search, a computationally cheap, topological model of failure cascading in a complex network (named the Motter‐Lai [ML] model) is used to simulate and quantify network resilience to cascading failures initiated by targeted attacks. The results on the 400 kV French power transmission network case study show that the proposed method allows us to identify optimal patterns of generators–distributors connection that improve cascading resilience at an acceptable cost. To verify the realistic character of the results obtained by the NSBDE with the embedded ML topological model, a more realistic but also more computationally expensive model of cascading failures is adopted, based on optimal power flow (namely, the ORNL‐Pserc‐Alaska) model). The consistent results between the two models provide impetus for the use of topological, complex network theory models for analysis and optimization of large infrastructures against cascading failure with the advantages of simplicity, scalability, and low computational cost.     

State of the Art in Risk Analysis of Workforce Criticality Influencing Disaster Preparedness for Interdependent Systems

The objective of this article is to discuss a needed paradigm shift in disaster risk analysis to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems. Much of the work that has been done on disaster risk analysis has focused primarily on preparedness and recovery strategies for disrupted infrastructure systems. The reliability of systems such as transportation, electric power, and telecommunications is crucial in sustaining business processes, supply chains, and regional livelihoods, as well as ensuring the availability of vital services in the aftermath of disasters. There has been a growing momentum in recognizing workforce criticality in the aftermath of disasters; nevertheless, significant gaps still remain in modeling, assessing, and managing workforce disruptions and their associated ripple effects to other interdependent systems. The workforce plays a pivotal role in ensuring that a disrupted region continues to function and subsequently recover from the adverse effects of disasters. With this in mind, this article presents a review of recent studies that have underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies for interdependent infrastructure and regional economic systems.     

基于复杂网络理论的股票指标关联性实证分析

复杂网络理论是研究股票市场内在结构和功能的有力工具,股票指标的关联性已成为研究股票市场的一个新视角。基于复杂网络理论对深沪300指数(2011年7月28至2012年2月28)构建网络拓扑结构。利用DFA进行数据筛选,计算筛选后数据的股票指标的绝对相关系数。根据股票指标的绝对相关系数概率分布确定阈值,构建网络拓扑结构,计算股票指标的网络统计特征指标,分析收益率、成交量、市盈率的网络结构。研究结果表明,股票收益率和成交量指标的具有较强的关联性,具有小世界性质;市盈率指标具有较弱的关联性,不具有小世界性质,是随机网络且受外界影响较大、效率低下,风险比较大。     

Interdependent Network Recovery Games

Recovery of interdependent infrastructure networks in the presence of catastrophic failure is crucial to the economy and welfare of society. Recently, centralized methods have been developed to address optimal resource allocation in postdisaster recovery scenarios of interdependent infrastructure systems that minimize total cost. In real-world systems, however, multiple independent, possibly noncooperative, utility network controllers are responsible for making recovery decisions, resulting in suboptimal decentralized processes. With the goal of minimizing recovery cost, a best-case decentralized model allows controllers to develop a full recovery plan and negotiate until all parties are satisfied (an equilibrium is reached). Such a model is computationally intensive for planning and negotiating, and time is a crucial resource in postdisaster recovery scenarios. Furthermore, in this work, we prove this best-case decentralized negotiation process could continue indefinitely under certain conditions. Accounting for network controllers' urgency in repairing their system, we propose an ad hoc sequential game-theoretic model of interdependent infrastructure network recovery represented as a discrete time noncooperative game between network controllers that is guaranteed to converge to an equilibrium. We further reduce the computation time needed to find a solution by applying a best-response heuristic and prove bounds on ε-Nash equilibrium, where ε depends on problem inputs. We compare best-case and ad hoc models on an empirical interdependent infrastructure network in the presence of simulated earthquakes to demonstrate the extent of the tradeoff between optimality and computational efficiency. Our method provides a foundation for modeling sociotechnical systems in a way that mirrors restoration processes in practice.     

自然异动下关键基础设施网络风险研究

关键基础设施保护是突发事件应急管理的重要对象构成,确定风险和保护排序是其保护机制的基本依据。在自然异动约束下,本文将信息扩散理论的超越概率引入风险分析模型,研究跨区域网络级联失效造成的风险,提出了确定关键基础设施网络构件的风险等级及保护排序的方法。以覆冰灾害下电网为例,研究了电网中不同支路在覆冰灾害下面临风险程度,与基本脆弱点进行对比,验证以异动风险作为保护参考的合理性。     

Design Improvements for Message Propagation in Malleable Social Networks

This study presents the formal problem definition and computational analysis of the network design improvements for idea and message propagation in both enterprise and consumer social networks (ESN and CSN, respectively). Message propagation in social networks is impacted by how messages are seeded in the network, and by propagation characteristics of the network topology itself. It has been recognized that the propagation properties of these networks can be actively influenced by network design interventions, such as the deliberate creation of new connections. We address the problem of finding cost‐effective message seeding, and identifying potential new network connections that allow improved propagation in social networks with cascade propagation. We use the hop‐constrained minimum spanning tree (HMST) model to find the seeds and possible new connections that result in networks with improved propagation properties. Moreover, we present new heuristic algorithms that substantially improve the solution quality for the HMST problem. Computational results posit that the design improvements proposed by the HMST approach can greatly improve cascade propagation performance of the networks at low cost.     

Resolution of reliability problems based on failure mode analysis: an integrated proposal applied to a mining case study

Abstract

Reliability determines, in large part, the operational productivity. Nevertheless, a frequent problem is the absence of effective mechanisms to support maintenance management. In particular, there is a need for methodologies focused on improving the detection and analysis of risks that affect reliability. This article presents a methodological proposal for the resolution of these problems, using a high-impact failure mode analysis. The methodology is based on four phases: identification of failure modes, ranking and criticality analysis of them, identification of the root cause(s) and search for highly effective solutions. Among the variety of tools that can be used, it is proposed the use of three specific tools: Criticality Analysis, which allows discrimination and ranking of phenomena and assets; Root Cause Analysis, which focuses on the identification of the real causes of the problems; and a tool for generation of effective and efficient solutions (TRIZ), which it is not usually applied to reliability problems. The proposal is applied in a mining filtration plant, identifying and classifying current problems and generating solutions.     

Methods for Managing Different Perspectives of Project Success

The results of an exploratory study of UK organizations into methods for meeting project key performance indicators (KPIs) are presented. The paper explores: influences on the use of methods to manage project KPIs; the need for and existence of methods; the factors that facilitate the meeting of the KPIs. It is concluded that the use of methods varies depending upon the perceived importance of the project, the type of project, the client-team relationship and whether an organization performance management system exists. With project management capability often decreasing, in part due to a failure to meet psychosocial project KPIs, a need for methods linked to the KPIs was identified. However, the study found relatively low levels of adoption of such methods. Where psychosocial project KPIs were being met the following facilitating factors emerged: top-level policies, organization-wide training, integration with existing management processes, building into project management system. Situations were found where decision-makers did not consider the psychosocial KPIs of programme and project managers. This was seen to contribute to a failure by organizations to manage necessary increases in their project management capability and to be acting as a possible barrier to long-term, sustainable improvements in performance.     

A decision support system for modelling and implementing the supply network configuration and operations scheduling problem in the machine tool industry

This paper presents a decision support system to simultaneously solve the supply network configuration problem and the operations scheduling problem for the machine tool industry. A novel database structure, which is able to consider alternative operations and alternative bills of material, has been used. An algorithm for complete enumeration to determine all the feasible solutions using stroke graphs is introduced. A multiagent-based simulator evaluates the different key performance indicators that the supply network deals with for each alternative solution (e.g. workload, profits, delivery times, etc.) to determine that ‘satisficed’ by the collaborative decision-making among its members. A case study based on a Spanish company that assembles highly customised machines and tools in several European plants is considered. From the experiments results based on data linked to this industry, it will be demonstrated that the tool is potentially useful for stakeholders and for the central decision-maker to make decisions collaboratively in a multisite context case.     

An integrated closed-loop product lifecycle management approach for reverse logistics design

Nowadays, reverse logistics is gaining importance for many companies in different industries. This importance is rooted in the fact that it generates profit and decreases the environmental impacts of products. Even though the decrease of environmental impacts is an indispensable requisite, reverse logistics design is only driven by cost indicators. The main reason behind this high cost is access to environmental information is difficult and is directly linked to data all along the lifecycle of the product. This paper presents a method by which reverse logistics design integrates environmental impacts based on the management of closed-loop product lifecycle. This method is divided into two processes: from beginning of life to end of life and from end of life to beginning of life. The first process integrates product data in order to calculate environmental impacts of the potential reverse logistics networks, whereas the second process selects the most appropriate reverse logistics network before optimising the product based on this particular network. The proposition is illustrated by a case study on a recycled aluminium automotive part.     

考虑自适应行为的研发网络风险传播模型构建及仿真

构建风险视域下研发网络企业自适应行为规则，基于SIS模型构建研发网络风险传播模型，运用数值仿真的方法通过改变模型参数探索在考虑自适应行为的情况下研发网络的风险传播规律，研究结果表明：（1）C1策略增强了网络的层次性和社团强度，一定程度上抑制了研发网络中风险的传播；C2策略下节点之间新连接的建立更多是基于临近性的考量，容易陷入路径依赖和能力陷阱；（2）研发网络企业的自适应行为会导致社团强度的涨落，平均路径长度的下降以及平均聚类系数的增长充分体现出C1策略的有效性。（3）C1策略下，断边概率p与I*之间呈现"U"型相关关系；在C2策略下随着断边概率p的增长I*逐渐降低。（4）在C1策略和C2策略下，随着参数ζ的增长I*也随之增长，可知组织依赖水平是研发网络风险传播控制中需要重点关注的因素。本文揭示了在考虑自适应行为的情况下研发网络的风险传播规律，为网络化运作背景下研发网络治理提供理论依据。     

考虑多维效率的上市公司财务困境预警研究

已有的财务困境预警研究一般基于财务指标，或在财务指标基础上引入单一效率指标，而多维效率指标能够更加全面有效地反映不同行业、不同资产规模的上市公司整体状况，从而对上市公司财务困境产生更好的预警效果。本文从经营效率、财务效率、融资效率和人力资本效率这四个维度分别提出相对应的投入产出指标体系，并采用数据包络分析对上市公司各个维度的相对有效性进行评价。在此基础上，将得到的多维效率指标与财务指标相融合，建立上市公司财务困境预警模型。为了验证所提出模型的有效性，采用支持向量机、人工神经网络和决策树这三种常用的财务困境预警技术，并基于不同的财务指标体系对我国上市公司进行实证研究。结果表明，考虑多维效率指标的上市公司财务困境预警模型能够有效提高预测准确度。     

Efficient Allocation of Resources for Defense of Spatially Distributed Networks Using Agent‐Based Simulation

This article presents ongoing research that focuses on efficient allocation of defense resources to minimize the damage inflicted on a spatially distributed physical network such as a pipeline, water system, or power distribution system from an attack by an active adversary, recognizing the fundamental difference between preparing for natural disasters such as hurricanes, earthquakes, or even accidental systems failures and the problem of allocating resources to defend against an opponent who is aware of, and anticipating, the defender's efforts to mitigate the threat. Our approach is to utilize a combination of integer programming and agent‐based modeling to allocate the defensive resources. We conceptualize the problem as a Stackelberg “leader follower” game where the defender first places his assets to defend key areas of the network, and the attacker then seeks to inflict the maximum damage possible within the constraints of resources and network structure. The criticality of arcs in the network is estimated by a deterministic network interdiction formulation, which then informs an evolutionary agent‐based simulation. The evolutionary agent‐based simulation is used to determine the allocation of resources for attackers and defenders that results in evolutionary stable strategies, where actions by either side alone cannot increase its share of victories. We demonstrate these techniques on an example network, comparing the evolutionary agent‐based results to a more traditional, probabilistic risk analysis (PRA) approach. Our results show that the agent‐based approach results in a greater percentage of defender victories than does the PRA‐based approach.     

A multi-criteria approach for managing inter-enterprise collaborative relationships

Collaboration amongst enterprises is a common strategy used to increase competitiveness. Thus, enterprises that are collaborating need to define and use performance measurement/management frameworks composed of performance elements (objectives, performance indicators, etc.) that facilitate the management of their activity, as well as monitor their strategy and processes. There are many factors, e.g. trust, interoperability of Information Systems, etc. that need to be managed properly in order to support collaborative success. However, such factors and performance are not usually managed together. Furthermore, these factors and performance elements are interrelated but these influences are commonly overlooked. This paper aims to present an approach based on the Analytic Network Process (ANP) to manage collaborative relationships under an integrated approach by considering not only the inter-enterprise performance elements, but also the factors that influence collaboration. The approach is then applied to a collaborative enterprise network belonging to the renewable energy sector. With this innovative approach, enterprises will obtain significant information for the decision-making process, regarding which are the factors and performance elements that have the greatest impact on their competitiveness, and therefore should be prioritized.