Evaluating Complex System Resilience Through BARD: A Bifurcation Analysis for Resilience Diagnostics Framework

Resilience engineering is a new and proactive attitude toward improving the safety and reliability of complex industrial systems such as oil and gas companies and petrochemical plants. This study aims to evaluate the effect of redundancy on a resilient system and determine which redundancy in different aspects including human resources and equipment items (hardware, software, and information) is more efficient in a resilient system through data envelopment analysis. Data collection was performed in a petrochemical plant through a questionnaire. Then, reliability of the data was calculated through Cronbach’s alpha test. Next, the effects of human redundancy and equipment redundancy on resilience engineering are examined by means of data envelopment analysis approach. The results show that efficiency of the resilient system is improved by considering both equipment redundancy and human redundancy. It is also revealed that human redundancy is more effective in resilient system performance compared to equipment redundancy. Hence, human redundancy plays a vital role in efficiency enhancement of resilient systems. This study is among the first ones that investigate the impact of the redundancy in resilient systems in different aspects including human and equipment redundancy by means of data envelopment analysis approach and highlights the gaps between managers and staff about the impact of equipment redundancy and human redundancy on resilience engineering.

One of today's major challenges is to integrate the different disciplines involved in the design, operation and maintenance of complex systems. Examples are transportation systems (cars, trains, airplanes…), energy production plants (renewable energy, thermal and nuclear power…), transportation and distribution networks (energy transmission and distribution, water distribution, gas transportation, road and railway networks) etc. Company executives, project managers, operation and design engineers need to grasp the opportunities coming from these complex, integrated systems while avoiding risks, to the extent possible. Decisions must be taken in uncertain environments: then, uncertainty needs to be handled, for effective and rational decision-making. The one-week PhD school aims at providing an advanced training on concepts, methods and tools for assessing and managing risks and opportunities in complex systems. The courses offered cover concepts and methods for the evaluation, management and control of technical risks, as well as the uncertainties in the evaluations for confident decision-support. The presented approaches are notably based on Systems Analysis, Systems Engineering, Applied Mathematics and Computer Science, The objective is to provide the participants with adequate tools for tackling the problem with scientific rigor. The acquired concepts, methods and tools constitute an essential part of the skills that researchers, engineers and managers must have. In the end, the courses provide the knowledge and competence needed to architect and operate complex systems, i.e. to make them efficient and reliable in operation, and resilient to major hazardous events. One key objective of the PhD school is to engage multi-disciplinary and multi-cultural teams of students in the definition, design and development of a scientific research process in the areas of interest for the disciplines of risk assessment and uncertainty analysis. To this aim, “mixed” teams of participants with different backgrounds and Institutions of origin are formed at the beginning of the PhD school, and charged with the definition and statement of a research problem to be addressed. The procedures and frames of work for the development of original solutions to the problems defined by the various teams are discussed in self-managed sessions at the end of each day. Engagement by industrial partners in the monitoring of the projects is offered. At the end of the week, the teams present the definitions of the problems and illustrate the research work that they intend to carry out for their original solutions. The teams have, then, to continue the work throughout the following semester, with the objective of producing scientific papers presenting the findings of the research. Once positively evaluated by the responsibles of the PhD school, the papers can be submitted to the appropriate international communities of peers for publication on international journals or presentation at international conferences. The benefits of the projects are expected to be an independent and direct exposure to: research problem definition and solution; multi-national, multi-cultural, multi-disciplinary, long-distance research work management.

Maintaining the performance of infrastructure-dependent systems in the face of surprises and unknowable risks is a grand challenge. Addressing this issue requires a better understanding of enabling conditions or principles that promote system resilience in a universal way. In this study, a set of such principles is interpreted as a group of interrelated conditions or organizational qualities that, taken together, engender system resilience. The field of resilience engineering identifies basic system or organizational qualities (e.g., abilities for learning) that are associated with enhanced general resilience and has packaged them into a set of principles that should be fostered. However, supporting conditions that give rise to such first-order system qualities remain elusive in the field. An integrative understanding of how such conditions co-occur and fit together to bring about resilience, therefore, has been less clear. This article contributes to addressing this gap by identifying a potentially more comprehensive set of principles for building general resilience in infrastructure-dependent systems. In approaching this aim, we organize scattered notions from across the literature. To reflect the partly self-organizing nature of infrastructure-dependent systems, we compare and synthesize two lines of research on resilience: resilience engineering and social-ecological system resilience. Although some of the principles discussed within the two fields overlap, there are some nuanced differences. By comparing and synthesizing the knowledge developed in them, we recommend an updated set of resilience-enhancing principles for infrastructure-dependent systems. In addition to proposing an expanded list of principles, we illustrate how these principles can co-occur and their interdependencies.

It is now widely recognized that many important events in the life cycle of complex engineering systems cannot be foreseen in advance. From its origin in ecological systems, operating without the use of foresight, resilience theory prescribes presuming ignorance about the future, and designing systems to manage unexpected events in whatever form they may take. However, much confusion remains as to what constitutes a resilient system and the implications for engineering systems. Taking steps toward a synthesis across a fragmented body of research, this paper analyses 251 definitions in the resilience literature, aiming to clarify key distinctions in the resilience concept. Asking resilience of what , to what , and how , we first distinguish systems serving higher ends and systems that are ends in themselves, and, within these, performance variables to be minimized, preserved, or maximized. Second, we distinguish systems subject to adverse events, adverse change, turbulence, favorable events, favorable change, and variation. Finally, we distinguish systems capable of recovery, absorption, improvement, graceful degradation, minimal deterioration, and survival. Together, these distinctions outline a morphology of resilient systems and suggest answers to the principal design questions, which must be asked of any resilient engineering system.

A Network Approach to Quantify the Resilience of Complex Systems

This paper will explore the concept of resilience from its roots in ecology to the application of this ecological concept of resilience to social and community resilience in the context of climate change. In this context, resilience is seen as a property of complex adaptive communities rather than of individuals. This paper will explore how this ecological concept of resilience has been taken up both by climate adaptation research and by the Transition Town movement. This ecological concept of resilience is at odds with the individualism of both psychological and economic approaches to resilience in relation to climate change.

Towards the Design of Resilient Large-scale Engineering Systems

BackgroundResilience is a system performance feature and integration in a management framework. The aim of resilience engineering (RE) is to improve the capacity to mitigate variations in system performance when faced with changes, disruptions, and uncertainties. Social network analysis (SNA) enables the examination of intricate human systems by visualizing and describing relationships among individuals, groups, and entities.ObjectiveThis paper is aimed at evaluating RE to improve safety management performance using an SNA approach.MethodsIn the present study, the databases ISI Web of Science, Scopus, PubMed Central, Science Direct, and Google Scholar were used. The keywords for the searches included 'resilience engineering', 'resilience', 'safety management', and 'social network analysis'.ResultsSNA, which is frequently employed to examine relationships and structures within networks, offers valuable tools for analyzing and enhancing the resilience of interconnected systems. SNA is a quantitative approach rooted in graph theory and mathematical symbols. The process of SNA entails conducting a survey to gather data from participants, refining the collected data, and utilizing software such as Gephi to map social networks.ConclusionsAssessing the RE of organizations through SNA can provide a better understanding of how individuals and groups interact, and how determining the nature of these interactions can impact safety management performance. Because appropriate communication among employees, as well as appropriate and desirable communication between managers and employees, and vice versa, can play a significant role in improving safety management performance. Appropriate and desirable communications by creating a strong structure led to an increase in the organization's ability to respond to safety issues, effective improvement, and active participation in accident prevention, ultimately playing a very important role in enhancing organizational resilience in safety management performance.

The aim of this paper is to provide practitioners and consultants with guidelines for enhancing organizational resilience in small and medium-sized businesses. It describes the actions an organization could perform and what goals should be pursued in order to successfully respond to potential threats. Design/methodology/approach – The paper builds on existing research on organizational resilience as well as the resilience of complex systems and the theories established there. By linking these research areas, it describes an adapted framework that considers the use of artificial intelligence and the provision of smart services in service companies. In addition, the paper illustrates the practicality of the framework by applying it to a small brick-and-mortar retail business.Findings – This paper proposes the use of a structured approach and methods such as stress testing and simulations to increase organizational resilience. It suggests that using artificial intelligence as well as offering smart services can have a positive impact on the resilience of a small and medium sized business.Originality/value – The paper contributes to the better applicability of measures aimed at increasing organizational resilience by suggesting a structured approach as well as appropriate avenues for action.Keywords: Organizational Resilience, Complex Systems, Smart Services, Artificial Intelligence

Resilient systems have the capability to survive and recover from seriously affecting events. Resilience engineering already is established for socio-economic organisations and extended network-like structures e. g. supply systems like power grids. Transferring the known principles and concepts used in these disciplines enables engineering resilient load-carrying systems and subsystems, too. Unexpected load conditions or component damages are summarised as disruptions caused by nesciense that may cause damages to the system or even system breakdowns. Disruptions caused by nescience can be controlled by analysing the resilience characteristics and synthesising resilient load-carrying systems. This paper contributes to a development methodology for resilient load-carrying systems by presenting a resilience applications model to support engineers analysing system resilience characteristics and behaviour. Further a concept of a systematically structured solution catalogue is provided that can be used for the classification of measures to realise resilience functions depending on system adaptivity and disruption progress. The resilience characteristics are illustrated by 3 examples.

Agent-based modelling and mental simulation for resilience engineering in air transport

Cyberphysical system is the integration of physical space (equipment, devices and people) with computing, communication and control systems (cyberspace). The National Institute of Standards and Terminology (NIST) has defined CFS as cyberphysical systems (CFS), which are designed systems built on the continuous integration of computational algorithms and physical components. Improving the CFS provides greater opportunities for performance, adaptability, scalability, stability, security and usability, far exceeding today's simple embedded systems. CFS technology will transform the interaction of people with the engineering system [1, 2]. Today, cyberphysical systems exist in various fields, such as automotive, aerospace, civil, railway, medical. Large productions seek to increase the availability of the asset, while reducing maintenance costs through cyberphysical systems. With the development of the industry and the use of systems in production, the proposed maintenance is used constantly to avoid failures. CFS maintenance is performed when certain indicators signal that the condition of the system has deteriorated. One way to solve this problem is to provide systems with resistance properties. Such systems are able to recover quickly and continue to function in changing conditions [3-9]. Therefore, the urgent task is to develop approaches that will predict the resilience of cyberphysical systems based on cloud computing. It is necessary to build methods and tools that will monitor the condition of the CFS and predict the timely replacement of their components that may fail. The application of the process of predicting the resilience of cyberphysical systems using cloud-oriented increases the effective reliability and availability of cyberphysical systems in its life cycle by identifying future failures and reducing unscheduled maintenance. The forecasting process involves the assessment of the useful life, and the implementation of a post-forecast decision on maintenance measures in accordance with the rules. The method of predicting the resilience of cloud-oriented cyberphysical systems allows to determine the state of the cyberphysical systems using cloud computing. The mechanism used to reduce the cost of maintenance and detailed planning of maintenance operations, the apparatus of genetic algorithms.

Resilience of complex systems in modern contexts: sense-making of deviations and enabling the human for mitigating unwanted events and incidents

The robustness and resilience of complex systems are crucial for maintaining functionality amid disruptions or intentional attacks. Many such systems can be modeled as networks, where identifying structurally central nodes is essential for assessing their robustness and susceptibility to failure. Traditional centrality metrics often face challenges in identifying structurally important nodes in networks exhibiting heterogeneity at the network scale, with multilayer networks being a prime example of such networks. These metrics typically fail to balance the trade-off between capturing local layer-specific structures and integrating global multiplex connectivity. In this study, we extend DomiRank centrality, a metric that has been shown to effectively assess nodal importance across diverse monoplex topologies, to multiplex networks. Our approach combines layer-specific DomiRank calculations with a global contextualization step, incorporating multiplex-wide DomiRank scores to combine rankings. Through synthetic and real-world network studies, we demonstrate that our generalized DomiRank framework significantly improves the identification of key nodes in highly heterogeneous multiplex networks. This work advances centrality-based robustness assessments by addressing the fundamental trade-off between layer adaptability and multiplex-wide coherence.

Engineered Resilient Systems: A DoD Perspective