Quantitative Resilience Assessment Research Articles

Towards resilience in Industry 5.0: application of system dynamics in matrix-structured manufacturing system

In the face of increasing global disruptions, resilient manufacturing systems are critical to ensuring operational stability, a concept central to the Industry 5.0 vision. The Matrix-Structured Manufacturing System (MMS) is a key example of such resilience, designed to maintain functionality and recover quickly under major disruptions. This paper introduces a novel System Dynamics (SD)-based modelling and simulation framework to quantitively assess the performance indicators and resilience of MMS. By simulating key disruption scenarios, including partial equipment failures and order fluctuations with resource constraints, this study evaluates the system’s response and recovery capabilities using a Comprehensive Resilience Index (CRI). Unlike conventional approaches, the proposed SD model captures the dynamic interactions across various production configurations and simulates the operational logic governing them, which can also be applied to other production systems. The simulation results demonstrate the potential of SD to support decision-making by providing insights into the effectiveness of reconfiguration strategies and resource management. This work aims at offering robust methods for quantitative resilience assessment and strategic planning towards the practical implementation of resilient manufacturing in Industry 5.0 envisions.

Catastrophe-related disruptions’ preparedness and emergency management in Morocco: a proactive risks and resilience digital twin-based analysis

Purpose The purpose of this study is to analyze the operations and performance dynamics of a supply chain (SC) subject to disruptions. The preparedness of Moroccan responders in handling emergencies could be enhanced significantly, by devising digital twin-based decision support systems (DSSs). Design/methodology/approach The authors create a discrete-event simulation model to investigate proactively risks and resilience of a Moroccan basic-items SC (BISC). In this study, the authors analyze the effects of catastrophe-related disruptions (CRDs) on the Moroccan BISC, by the use of a simulation-based decision-supporting quantitative method. Findings In the disruption-free simulation experiment, the outcome was a satisfactory 100% coverage. By implementing CRDs, inventory levels have dropped, service levels decreased, lead time raised and there was an increase in backlogged products and late orders numbers. The highest impact was observed for the shutdown of paths linking suppliers to warehouses, whereas the increase in demand had a comparatively minor effect. The risk analysis approach helps to identify critical products for which the time-to-recover is longer and requires more commitment to enhance their resilience. Practical implications The model serves to deduce quantitative resilience assessment from simulation, streamline the selection of recovery strategies and enable the best-informed reactive decision-making to minimize the impact. Originality/value The research brings organizing solutions to catastrophe-related emergencies in Morocco. It would contribute significantly by visualizing, examining and unveiling the effects of disruptions on a BISC and offering actionable recommendations for remedial measures.

A novel framework for quantitative resilience assessment in complex engineering systems during early and late design stages

Recently, resilience assessment has evolved and grown in prominence, yet most studies are carried out on operational stage when sufficient knowledge on the processes is available, overlooking the design stage, a time frame that is more suitable for making a resilient system. To this end, this work aims at developing a novel quantitative resilience assessment framework for engineering systems with two different approaches that practically analyse resilience at the early and late design stages when detailed information on the system’s safety and resilience capabilities may be deficient. In the early design stage, system resilience attributes are identified, and expert judgment is used to assess their quality. In the late design stage, attributes are derived from revealed information such as detailed emergency response and safety barrier data. In both stages, dynamic Bayesian Network (DBN) is used to quantify resilience based on the acquired information. Since the green hydrogen technology is relatively novice, the application of the proposed framework is demonstrated in a resilience assessment of a green hydrogen plant undergoing hydrogen release scenarios. The proposed framework can be used as an effective tool for early design improvements as well as enhancing process safety in the late design stage of hydrogen plants or any other complex engineering system.

An integrated approach to quantitative resilience assessment in process systems

Chemical process systems are becoming more automated and complex, which leads to increased interaction and interdependence between the human and technical elements of process systems. This urges the need for updating the safety assessment method by treating “safety” as an emergent property of a system. Uncertainty comes together with complexity. To enhance system ability of dealing with uncertain disruptions, this paper proposes a quantitative resilience assessment method by modeling the failure propagation (initiated by a disruption) across the functional units of a system. The Functional Resonance Analysis Method (FRAM) is utilized to model the system operation to represent the relationship among its function units and to consider the interactions among human-technical factors. Then, a Cascading Failure Propagation Model (CFPM) is developed to quantify the fault propagation process and reflect the system functionality changes over time for resilience assessment. The proposed method is applied to a propane-feeding control system. The results show that it can help practitioners understand the process of fault propagation and risk increase, identify potential ways to design a more resilient system to respond to uncertain disruptions/attacks, and provide a real-time dynamic resilience profile to support decision-making.

A multi-stage quantitative resilience analysis and optimization framework considering dynamic decisions for urban infrastructure systems

In urban infrastructure systems, resilience is crucial for maintaining functionality, minimizing losses, and expediting recovery during disruptive incidents. Effective allocating resources across various phases of emerging disturbances can generally enhance the system's capacity to cope with disasters. However, it is imperative to recognize that distinct resource allocation strategies may lead to divergent outcomes in terms of resilience performance. Therefore, this study develops a framework for optimizing resource allocation based on multiple resilience objectives by understanding the interplay between resilience performance and dynamic decision-making. The resilience processes are first formalized into distinct stages, considering the technical and organizational resilience of the infrastructure system in the event of disruption. Building upon this foundation, five decision scenarios are proposed, contingent on the allocation or non-allocation of resources to each resilience stage. A multi-resilience-objective mixed-integer linear programming (MROMILP) model is formulated to optimize the resource allocation scheme for each resilience stage within the constraints of internal resources. Finally, the model and framework are tested using a power system as a tangible example. The integrated multi-stage quantitative resilience assessment and optimization method proposed in this study can assist decision-makers in making dynamic and continuous trade-offs between resources and resilience targets.

On the quantitative resilience assessment of complex engineered systems

Recent years have seen the increasing complexity of engineered systems. Complexity and uncertainty also exist in engineered systems’ interactions with human operators, managers, and the organization. Resilience, focusing on a system’s ability to anticipate, absorb, adapt to, and recover from disruptive situations, can provide an umbrella concept that covers reliability and risk-based thinking to ensure these complex systems' safety. This paper discusses the quantitative aspects of the notion of resilience. Like the quantitative risk assessment framework, a generic framework should be developed for quantitative resilience assessment. This paper proposes a framework based on a triplet resilience definition consisting of disruption, functionality, and performance. Uncertainty treatment is also considered. The proposed framework aims to answer the question of “resilience of what to what” and how it can be quantitively assessed.

Multi-Component Resilience Assessment Framework for a Supply Chain System

The goal of this paper is to develop a quantitative resilience assessment framework for a supply chain system exposed to multiple risk factors. Most existing studies on supply chain resilience have primarily focused on assessing the system’s ability to withstand and recover from disruptions caused by a single type of hazard. However, a supply chain system is exposed to multiple exogenous and endogenous events and conditions over a planning horizon, and a comprehensive assessment of resilience should take into account multiple risk factors. Moreover, contrary to the conventional resilience assessment methods focusing on the short duration during which the system is impacted by a disaster event, the proposed framework measures the resilience capacities of the system over a long-term horizon through multi-risk assessment and multi-component resilience assessment. Specifically, a new multi-component resilience index is proposed to measure (a) hazard-induced cumulative loss of functionality, (b) opportunity-induced cumulative gain of functionality, and (c) non-hazard-induced cumulative loss of functionality. The case study results indicate that all three types of risk factors contribute to the overall resilience index significantly and ignoring any one of them may result in inaccurate supply chain performance and resilience assessment.

Methodology for Resilience Assessment of Oil Pipeline Network System Exposed to Earthquake

The oil pipeline network system (OPNS) is an essential part of the critical infrastructure networks (CINs), and is vulnerable to earthquakes. Assessing and enhancing the resilience of the OPNS can improve its capability to cope with earthquakes or to recover the system’s performance quickly after the disturbance. This study defines the concept of OPNS resilience in the resistive ability, the adaptive ability, and the recovery ability. Then, the quantitative resilience assessment model is established considering the earthquake intensities, the role of safety barriers, the time-variant reliability, and the importance coefficient of each subsystem via a Monte Carlo simulation. Combining the model with GIS technology, a new methodology to evaluate OPNS resilience is proposed, and the resilience partition technology platform is developed, which can visualize the results of the resilience assessment. Finally, a case study is implemented to demonstrate the developed methodology, and a discussion is provided to identify the sensitive variables. The proposed resilience methodology can provide a framework for the probabilistic resilience assessment of OPNS, and could be expanded to other lifeline network systems.

Thinking in Systems, Sifting Through Simulations: A Way Ahead for Cyber Resilience Assessment

The interaction between the physical world and information technologies creates advantages and novel emerging threats. Cyber-physical systems (CPSs) result vulnerable to cyber-related disruptive scenarios, and, for some critical systems, cyber failures may have fallouts on society and environment. Traditional risk analysis in no more sufficient to deal with these problems. New techniques are gaining increasing consensus, especially those based on systems theory. In this context, the System-Theoretic Process Analysis for Security (STPA-Sec) extends the Systems-Theoretic Accident Modelling and Processes (STAMP) model considering cyber threats, and identifying unsafe and unsecure controls throughout a cyber socio-technical system. Despite its large usage as a descriptive tool, there is still limited use of STPA-Sec in (semi-)quantitative terms. This article presents System-Theoretic Process Analysis for Security with Simulations (STPA-Sec/S), a methodological interface between STPA-Sec and quantitative resilience assessment based on simulation models. The methodology is instantiated in a demonstrative case study of a water treatment plant, and its critical CPSs which may impact both community health, and environment. The obtained results show how STPA-Sec/S foster systems understanding, allow a systematic identification of its major criticalities, and the respective quantification.

Resilience assessment of chemical industrial areas during Natech-related cascading multi-hazards

In chemical industrial areas, technological accidents triggered by natural events (Natech events) may escalate. Complex cascading multi-hazard scenarios with high uncertainties may be caused. Resilience is an essential property of a system to withstand and recover from disruptive events. The present study focuses on the change of the resilience level due to (possible) interactions between cascading hazards, chemical installations and safety barriers during the dynamic evolution of fire escalations triggered by a natural hazard (certain cascading multi-hazard scenarios). A quantitative resilience assessment method is developed to this end. The state transition of a system facing accidents in the context of resilience is explored. Moreover, the uncertainties accompanying an accident evolution are quantified using a Dynamic Bayesian Network, allowing a detailed analysis of the system performance in different time steps. System resilience is measured as a time-dependent function with respect to the change of system performance. The applicability of the proposed methodology is demonstrated by a case study, and the effects of different configurations of safety barriers on improving resilience are discussed. The results are valuable to support disaster prevention within chemical industrial areas.

Quantitative resilience assessment of the network-level metro rail service's responses to the COVID-19 pandemic

The metro rail system has proven to be the most efficient high-capacity carriers. During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions become a widely adopted strategy to limit physical movements and interactions. For situational awareness and decision support, data-driven analytics about serviceability are invaluable to metro agencies and decision-makers of cities. This paper presents a data-driven analytical framework that quantitatively evaluates COVID-19-caused resilience performance of metro rails. Several characteristics (e.g., vulnerability, robustness, rapidity, and degree to return) of the metro system's responses to the disturbance were identified and modeled with multivariate multiple regression. The applicability and rationality of the resilience evaluation model were validated by the metro transit data of the United States. The preliminary results disclosed that metro rail transit encountered more vulnerability (90.6%) in passenger trips than motorbus and light rail (around 70%). A set of statistical models were employed to disentangle the effect of socio-demographic variables and COVID-19-related factors on the metro transit. The disclosed emerging knowledge of resilience provides an in-depth understanding of mobility trends for the public and time-sensitive decision support for the policy effects, to further improve the service and management of the metro system under the spread of the epidemic.

Resilience Assessment of an Urban Metro Complex Network: A Case Study of the Zhengzhou Metro

An urban metro network is susceptible to becoming vulnerable and difficult to recover quickly in the face of an unexpected attack on account of the system’s complexity and the threat of various emergencies. Therefore, it is necessary to assess the resilience of urban metro networks. However, the research on resilience assessment of urban metro networks is still in the development stage, and it is better to conduct said research using a technique which combines many attributes, multiple methods, and several cases. Therefore, based on the complex network modeling and topological characteristics analysis of metro systems, a metro network’s robustness and vulnerability measurement method under node interruption and edge failure is proposed for the first time in this study. Then, considering the three cases of general station interruption, interchange station interruption, and traffic tunnel failure, a quantitative resilience assessment model of metro networks is put forward, and the corresponding recovery strategies are discussed. Finally, a case study of the Zhengzhou Metro Network (ZZMN) under an extreme rainstorm is conducted to demonstrate the viability of the proposed model. The results show that ZZMN possesses scale-free and small-world network properties, and it is robust to random interruptions but vulnerable to deliberate attacks. ZZMN still needs to improve its effectiveness in information transmission. The centrality distribution for each node in the ZZMN network differs significantly, and each node’s failure has a unique impact on the network. The larger the DC, BC, and PR of a node is, the lower the network’s robustness after its removal is, and the stronger the vulnerability is. Compared with the three cases of general station interruption, interchange station interruption, and traffic tunnel failure, the network loss caused by tunnel failure was the lowest, followed by general station interruption, and the interruption at interchange stations was the most costly. Given the failures under various cases, the metro management department should prioritize selecting the optimal recovery strategy to improve the resilience of the metro network system. This study’s findings can assist in making urban metro systems less vulnerable to emergencies and more resilient for a quick recovery, which can provide scientific theoretical guidance and decision support for the safety and resilient, sustainable development of urban metro systems.

Resilience assessment of the downstream oil supply chain considering the inventory strategy in extreme weather events

As climate change intensifies and extreme weather events occur more frequently, the uncertainties of demand, production and inventory will affect the operation and management of supply chains. In this paper, a systematic method for evaluating the resilience of the supply chain is proposed, which is applied to the demand side management of an actual downstream oil supply chain. According to the practical operation of the downstream oil supply chain, several scenarios such as disruption of refineries, shutdown of pipelines and increasing demand are defined. Then, two indices are introduced to make a quantitative resilience assessment for the studied downstream oil supply chain. The inventory strategy is also proposed and applied to different scenarios. Finally, Monte Carlo simulation is used to make a comprehensive analysis with multiple scenarios. The results show that increasing the inventory of storage depots can improve the resilience of the supply chain by 2.67% to 14.83%. According to the sensitivity analysis for the failure rate and repair rate of each link, it is indicated that the resilience can be improved by 5.95% when the repair rate is doubled and be reduced to 16.04% when the repair rate is reduced by half.

Lifetime Resilience Migration Quantification Using Nonparametric Distance Metrics and Application for River-Crossing Bridges

Quantitative resilience assessment is an essential to the lifetime management of civil structures. With the uncertainties arising from both physical and socioeconomic dimensions, resilience needs to be measured probabilistically. Although existing resilience frameworks have addressed this need, none statistically characterize how much a system’s resilience migrates and in what direction it migrates. This paper proposes a lifetime resilience migration quantification (LRMQ) framework based on nonparametric distance metrics. In this framework, system resilience positioning is proposed by comparing the resilience of a variable system against two reference systems. Two decision-making analytics, a binary resilience classification (BRC) diagram and a lifetime resilience attenuation (LRA) model, are proposed. The proposed method is evaluated with success by performing numerical experimentation over a representative river-crossing bridge. It is observed that the bridge’s system resilience attenuates progressively in its lifetime; with the consideration of a scour countermeasure, resilience is effectively enhanced via visualizing the proposed BRC diagram and LRA model.

A STAMP-based approach to quantitative resilience assessment of chemical process systems

Chemical process systems (CPSs) involve complex dynamic processes. Besides, the emergent and uncertain hazards and disruptions cannot be identified entirely and prevented by conventional methods. In those situations, resilience for CPSs plays an essential role in absorbing, adapting to disruptions, and restoring from damages. Systemic modeling plays a vital role in assessing resilience. A system-based analysis model, system-theoretic accident model, and process (STAMP) can provide a robust framework. This paper develops a comprehensive methodology to systematically model and assess system resilience. The STAMP is employed to model and analyze the system safety of a process system. A new method of dynamic resilience assessment is then proposed to quantify the resilience of the system. The proposed method is applied to the diesel oil hydrogenation system. The results show that it quantifies the resilience of complex process systems considering human and organizational factors in a dynamic manner.

Resilience Assessment and Improvement for Cyber-Physical Power Systems Under Typhoon Disasters

The cyber-physical deep coupling makes power systems face more risks under small-probability and high-risk typhoon disasters. Resilience describes the ability of cyber-physical power system (CPPS) withstanding extreme disasters and resuming normal operation. To improve the resilience assessment and analysis method of CPPS, first, a CPPS resilience assessment framework that considers the space-time metrics of disasters and the interactions of information systems and power grids is proposed, including fault scenarios extraction, response and recovery analysis, quantitative assessment of resilience. Second, from the perspective of the geographical coupling between OPGW and transmission lines and the control coupling between automatic generation control system (AGC), substation automation system (SAS) and power system, the interaction of information flow and energy flow during the failure period is analyzed. The network flow theory is used to establish an information network traffic model to describe the operating status of the information system at each stage. On this basis, a mixed integer linear programming model for DC optimal power flow considering the information network constraints and a multi-stage bi-level model for cyber-physical collaborative recovery are established. Finally, we take the IEEERTS-79 system as an example to show that the proposed method can improve the quantization accuracy comparing with the assessment method of the conventional power system, and evaluate the enhancement of typical measures at different stages.

Probabilistic seismic resilience quantification of a reinforced masonry shear wall system with boundary elements under bi-directional horizontal excitations

The concept of resilience is gaining increased attention in disaster management due to the recent awareness of the need to reduce the detrimental post-event effects of natural disasters, e.g., earthquakes. Resilience is a practical concept that includes pre-event (preparedness and mitigation) and post-event (response and recovery) activities. Quantitative resilience assessment approaches are needed to compare the available mitigation strategies to decide on the most suitable strategy and provide better support for decision-making procedures. In this study, a methodology for quantifying the seismic resilience of reinforced masonry shear wall (RMSW) buildings with end-confined masonry boundary elements is implemented. The uncertainties associated with structural and non-structural losses and estimated recovery time uncertainties are considered while quantifying the resilience index of RMSW buildings. The archetype buildings studied have 8-, 10-, and 12-storey heights and are located in Vancouver, representing a high seismic zone in Canada. First, a numerical model was developed using OpenSees to derive the fragility surface for the studied archetypes subjected to bi-directional horizontal excitation. Second, a Monte Carlo simulation was performed to quantify the resilience index of each archetype considering the above-mentioned uncertainties. The results prove the robustness of ductile RMSW buildings having end-confined MBEs in mitigating the losses associated with disaster events. Additionally, the findings provide comprehensive and valuable information for earthquake mitigation measures and disaster risk reduction programmes.

Simulation-based assessment of supply chain resilience with consideration of recovery strategies in the COVID-19 pandemic context

In the wake of the COVID-19 pandemic, many firms lacked a strategy to cope with disruptions and maintain resiliency. In this study, we develop a measurement method to evaluate the impact of resilience strategies in a multi-stage supply chain (SC) in the presence of a pandemic. For the first time, we propose a method to deduce quantitative resilience assessment from simulation. We implement two resilience strategies, i.e., prepositioning extra-inventory and a backup supplier, and then we simulate its impact on SC resilience and financial performance. The simulation results indicate that the extra inventory leads to a higher resilience than a backup supplier but costs more for the given contextual setting. Finally, we examine the demand fulfillment and observe that the extra-inventory strategy allows for a higher service level, confirming our resilience simulations. We discuss the managerial implications of these findings on the descriptive and predictive analysis levels. Decision-makers can utilize our model and findings to develop a response plan in the occurrence of a pandemic or any long-duration high magnitude disruption. Also, scholars and managers can use our proposed method to measure SC resiliency from simulation in any disruption.

Development of Assessment Methods of Target-Driven Systems Resilience

Existing approaches to formation of quantitative indicator of resilience of objects of different complexity operating under conditions of external impacts are discussed. Advantages and disadvantages of approaches under discussion are specified. As general basis for application of resilience concept, the class of so-called target-driven systems oriented to achieving some preset target in a certain period is marked out. It is proposed to use the value of the indicator constructed using methodical apparatus "difficulties of achieving target" of I. B. Russman for quantitative assessment of resilience of similar systems. Methods of calculation and results of model assessments of resilience using this approach are presented. Directions of Russman’s approach development are proposed based on application of so-called method of trajectory analysis of behavior of target-driven systems, described by dynamics of integral indicator characterizing object of study in coordinates of rate and acceleration of its changes.

Quantitative resilience assessment of chemical process systems using functional resonance analysis method and Dynamic Bayesian network

The emergent hazards of chemical process systems cannot be wholly identified and are highly uncertain due to the complicated technical-human-organizational interactions. Under uncertain and unpredictable circumstances, resilience becomes an essential property of a chemical process system that helps it better adapt to disruptions and restore from surprising damages. The resilience assessment needs to be enhanced to identify the accident's root causes on the level of technical-human-organizational interactions, and development of the specific resilience attributes to withstand or recover from the disruptions. The outcomes of resilience assessment are valuable to identify potential design or operational improvements to ensure complex process system functionality and safety. The current study integrates the Functional Resonance Analysis Method and dynamic Bayesian Network for quantitative resilience assessment. The method is demonstrated through a two-phase separator of an acid gas sweetening unit. Aspen Hysys simulator is applied to estimate the failure probabilities needed in the resilience assessment model. The study provides a useful tool for rigorous quantitative resilience analysis of complex process systems on the level of technical-human-organizational interactions.