A fuzzy dynamic inoperability input-output model for strategic risk management in global production networks

Strategic decision making in Global Production Networks (GPNs) is quite challenging, especially due to the unavailability of precise quantitative knowledge, variety of relevant risk factors that need to be considered and the interdependencies that can exist between multiple partners across the globe. In this paper, a risk evaluation method for GPNs based on a novel Fuzzy Dynamic Inoperability Input Output Model (Fuzzy DIIM) is proposed. A fuzzy multi-criteria approach is developed to determine interdependencies between nodes in a GPN using experts’ knowledge. An efficient and accurate method based on fuzzy interval calculus in the Fuzzy DIIM is proposed. The risk evaluation method takes into account various risk scenarios relevant to the GPN and likelihoods of their occurrences. A case of beverage production from food industry is used to showcase the application of the proposed risk evaluation method. It is demonstrated how it can be used for GPN strategic decision making. The impact of risk on inoperability of alternative GPN configurations considering different risk scenarios is analysed

System-level prognostics based on inoperability input-output model

International audienceNowadays, the modern industry is increasingly demanding the availability and reliability of production systems as well as the reduction of maintenance costs. The techniques to achieving these goals are recognized and discussed under the term of Prognostics and Health Management (PHM). However, the prognostics is often approached from a component point of view. The system-level prognostics (SLP), taking into account interdependencies and multi-interactions between system components, is still an underexplored area. Inspired from the inoperability input-output model (IIM), a new approach for SLP is proposed in this paper. The inoperability corresponds to the component’s degradation, i.e. the reduction of its performance in comparison to an ideal reference state. The interactions between component degradation and the effect of the environment are included when estimating the inoperability of components and also when predicting the system remaining useful life (SRUL). This approach can be applied to complex systems involving multi-heterogeneous components with a reasonable computational effort. Thus, it allows overcoming the lack of scope and scalability of the traditional approaches used in PHM. An illustrative example is presented and discussed in the paper to highlight the performance of the proposed approach

Risk Assessment Methodology for Critical Infrastructure Protection

The European Programme for Critical Infrastructure Protection is the main vehicle for the protection of critical infrastructures in Europe. The Directive 2008/114/EC is the legislative instrument of this programme. Risk assessment is an important element that is mentioned throughout the Directive text. However, there is no harmonized methodology in Europe for the assessment of interconnected infrastructures. The present work describes such a methodology and its implementation for the assessment of critical infrastructures of European dimension. The methodology accounts for impact at asset level, evaluates the propagation of a failure at network level due to interdependencies and assess the economic impact of critical infrastructure disruption at national level.JRC.G.6-Security technology assessmen

ROBUST DECISION-MAKING AND DYNAMIC RESILIENCE ESTIMATION FOR INTERDEPENDENT RISK ANALYSIS

When systems and subsystems are put under external shocks and duress, they suffer physical and economic collapse. The ability of the system components to recover and operate at new stable production levels characterizes resilience. This research addresses the problem of estimating, quantifying and planning for resilience in interdependent systems, where interconnectedness adds to problem complexity. Interdependence drives the behavior of sectors before and after disruptions. Among other approaches this study concentrates on economic interdependence because it provides insights into other levels of interdependence. For sectors the normalized losses in economic outputs and demands are suitable metrics for measuring interdependent risk. As such the inoperability input-output model enterprise is employed and expanded in this study to provide a useful tool for measuring the cascading effects of disruptions across large-scale interdependent infrastructure systems. This research defines economic resilience for interdependent infrastructures as an "ability exhibited by such systems that allows them to recover productivity after a disruptive event in a desired time and/or with an acceptable cost". Through the dynamic interdependent risk model resilience for a disrupted infrastructure is quantified in terms of its average system functionality, maximum loss in functionality and the time to recovery, which make up a resilience estimation decision-space. Estimating such a decision-space through the dynamic model depends upon the estimation of the rate parameter in the model. This research proposes a new approach, based on dynamic data assimilation methods, for estimating the rate parameter and strengthening post-disaster resilience of economic systems. The solution to the data assimilation problem generates estimates for the rate of resilient recovery that reflects planning considerations interpreted as commodity substitutions, inventory management and incorporating redundancies. The research also presents a robust optimization based risk management approach for strengthening interdependent static resilience estimation. There is a paucity of research dealing with quantification and assessment of uncertainties in interdependency models. The focus here is more on the extreme bounds of event and data uncertainties. The deterministic optimization becomes a robust optimization problem when extremes of uncertainties are considered. Computationally tractable robust counterparts to nominal problems are presented here. Also presented in this research is a discrete event simulation based queuing model for studying multi-modal transportation systems with particular focus on inland waterway ports. Such models are used for impact analysis studies of inland port disruptions. They can be integrated with the resilience planning methodologies to develop a framework for large-scale interdependent risk and recovery analysis

Electricity Supply Interruptions: Sectoral Interdependencies and the Cost of Energy Not Served for the Scottish Economy

The power sector has a central role in modern economies and other interdependent infrastructures rely heavily upon secure electricity supplies. Due to interdependencies, major electricity supply interruptions result in cascading effects in other sectors of the economy. This paper investigates the economic effects of large power supply disruptions taking such interdependencies into account. We apply a dynamic inoperability input–output model (DIIM) to 101 sectors (including households) of the Scottish economy in 2009 in order to explore direct, indirect, and induced effects of electricity supply interruptions. We then estimate the societal cost of energy not supplied (SCENS) due to interruption, in the presence of interdependency among the sectors. The results show that the most economically affected industries, following an outage, can be different from the most inoperable ones. The results also indicate that SCENS varies with duration of a power cut, ranging from around £4300/MWh for a one-minute outage to around £8100/MWh for a three hour (and higher) interruption. The economic impact of estimates can be used to design policies for contingencies such as roll-out priorities as well as preventive investments in the sector

System-level prognostics based on inoperability input-output model

Nowadays, the modern industry is increasingly demanding the availability and reliability of production systems as well as the reduction of maintenance costs. The techniques to achieving these goals are recognized and discussed under the term of Prognostics and Health Management (PHM). However, the prognostics is often approached from a component point of view. The system-level prognostics (SLP), taking into account interdependencies and multi-interactions between system components, is still an underexplored area. Inspired from the inoperability input-output model (IIM), a new approach for SLP is proposed in this paper. The inoperability corresponds to the component’s degradation, i.e. the reduction of its performance in comparison to an ideal reference state. The interactions between component degradation and the effect of the environment are included when estimating the inoperability of components and also when predicting the system remaining useful life (SRUL). This approach can be applied to complex systems involving multi-heterogeneous components with a reasonable computational effort. Thus, it allows overcoming the lack of scope and scalability of the traditional approaches used in PHM. An illustrative example is presented and discussed in the paper to highlight the performance of the proposed approach

Risk-based evaluation and management of cyber-physical-social systems during pandemic crisis

This research focuses on risk-based evaluation and management of cyber-physical-social systems during a pandemic crisis. The ongoing novel coronavirus (COVID-19) epidemic has caused serious challenges for the world’s countries. The health and economic crisis caused by the COVID-19 pandemic highlights the necessity for a deeper understanding and investigation of the best mitigation policy. While different control strategies in the early stages, such as lockdowns and school and business closures, have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses and some controversial impacts on social justice. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative socio-economic impact of control strategies. To maximize the effectiveness of the controlling policies during a major crisis like a pandemic, we propose two mathematical frameworks which optimize the contorting policy while considering three sets of important factors, including the epidemiologic, economic, and social impact of the pandemic. Each formulation quantifies the epidemiologic impact using a modified SIRD (susceptible-infected-recovered-deceased) model which captures the number of infected, recovered, immune, and deceased populations. The two formulations propose different approaches for measuring the social and economic impacts of the pandemic. In the first formulation, the economic impact is a twofold measure, first the unmet demand because of supply perturbation (due to industry closure), and the second is the local business shrinkage because of demand perturbation (due to the state closure). The modified SIRD model is combined with a multi-commodity maximum network flow problem (MNFP) in which the unmet demand is measured in a network of states and industries. The proposed formulation is implemented on a dataset that includes 11 states, the District of Columbia (including the states in New England and the mid-Atlantic), and 19 industries in the US. In the second formulation, the economic impact is measured using the supply side multi-regional inoperability input-output model, accounting for the inoperability of each industry to satisfy the demand of final consumers and other industries, due to its closure. Also, the second formulation measures the social impact of the pandemic policy, by incorporating the vulnerability of social communities to get infected due to state opening or to lose their job due to the closure of the state. We test the efficacy of proposed formulations on the real data set of COVID-19 applicable to 50 states, the District of Columbia, and 19 industries. Both formulations are multi-objective mixed integer programming with three objectives which are solved using the augmented ε-constraint approach. The final pandemic policy is selected from the set of Pareto-optimal solutions based on the least cubic distance of the solution from the optimal value of each objective. The Pareto-optimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiologic impact change in the opposite direction, and it is more effective to close most states while keeping the majority of industries open. For each Pareto optimal solution, the unmet demand and the propagation of inoperability to the industries and states can be tracked down. This will give a holistic view of the impact of the pandemic policy on the health, economy, and social justice aspects of the country

Towards system-level prognostics : Modeling, uncertainty propagation and system remaining useful life prediction

Prognostics is the process of predicting the remaining useful life (RUL) of components, subsystems, or systems. However, until now, the prognostics has often been approached from a component view without considering interactions between components and effects of the environment, leading to a misprediction of the complex systems failure time. In this work, a prognostics approach to system-level is proposed. This approach is based on a new modeling framework: the inoperability input-output model (IIM), which allows tackling the issue related to the interactions between components and the mission profile effects and can be applied for heterogeneous systems. Then, a new methodology for online joint system RUL (SRUL) prediction and model parameter estimation is developed based on particle filtering (PF) and gradient descent (GD). In detail, the state of health of system components is estimated and predicted in a probabilistic manner using PF. In the case of consecutive discrepancy between the prior and posterior estimates of the system health state, the proposed estimation method is used to correct and to adapt the IIM parameters. Finally, the developed methodology is verified on a realistic industrial system: The Tennessee Eastman Process. The obtained results highlighted its effectiveness in predicting the SRUL in reasonable computing time

BAYESIAN KERNEL METHODS FOR THE RISK ANALYSIS AND RESILIENCE MODELING OF CRITICAL INFRASTRUCTURE SYSTEMS

The protection of critical infrastructures has recently garnered attention with an emphasis on analyzing the risk and improving the resilience of such systems. With the abundance of data, risk managers should be able to better inform preparedness and recovery decision making under uncertainty. It is important, however, to develop and utilize the necessary methodologies that bridge between data and decisions. The goal of this dissertation is to (i) predict the likelihood of risk, (ii) assess the consequences of a disruption, and (iii) inform preparedness and recovery decision making. This research presents a data-driven analysis of the risk and resilience of critical infrastructure systems. First, a new Bayesian kernel model is developed to predict the frequency of failures and a Beta Bayesian kernel model is deployed to model resilience-based importance measures. Bayesian kernel models were developed for Gaussian distributions and later extended to other continuous probability distributions. This research develops a Poisson Bayesian kernel model to accommodate count data. Second, interdependency models are integrated with decision analysis and resilience quantification techniques to assess the multi-industry economic impact of critical infrastructure resilience and inform preparedness and recovery decision making under uncertainty. Examples of critical infrastructure systems are inland waterways, which are critical elements in the nation’s civil infrastructure and the world’s supply chain. They allow for a cost-effective flow of approximately $150 billion worth of commodities annually across industries and geographic locations, which is why they are called “inland marine highways.” Aging components (i.e., locks and dams) combined with adverse weather conditions, affect the reliability and resilience of inland waterways. Frequent disruptions and lengthy recovery times threaten regional commodity flows, and more broadly, multiple industries that rely on those commodities. While policymakers understand the increasing need for inland waterway rehabilitation and preparedness investment, resources are limited and select projects are funded each year to improve only certain components of the network. As a result, a number of research questions arise. What is the impact of infrastructure systems disruptions, and how to predict them? What metrics should be used to identify critical components and determine the system’s resilience? What are the best risk management strategies in terms of preparedness investment and recovery prioritization? A Poisson Bayesian kernel model is developed and deployed to predict the frequency of locks and dams closures. Economic dynamic interdependency models along with stochastic inoperability multiobjective decision trees and resilience metrics are used to assess the broader impact of a disruption resulting in the closure of a port or a link of the river and impacting multiple interdependent industries. Stochastic resilience-based measures are analyzed to determine the critical waterway components, more specifically locks and dams, that contribute to the overall waterway system resilience. A data-driven case study illustrates these methods to describe commodity flows along the various components of the U.S. Mississippi River Navigation System and employs them to motivate preparedness and recovery strategies