Management Of Complex Systems Research Articles

Knowledge-oriented management systems for complex objects

The article examines the issue of improving the efficiency of complex systems management through the use of knowledge-based methods and technologies. The article emphasizes the need to revise traditional approaches to scientific research and knowledge management by introducing a transdisciplinary paradigm and artificial intelligence to achieve sustainable development and knowledge evolution. The authors try to solve the problem of management in the face of uncertainty and incomplete information, which is typical for such systems. The methods of integrating knowledge from different fields to create an integrated management system are considered. The basis of management is the holarchic structure, which organizes the system in the form of a hierarchy of holons. Each holon has a specific role and knowledge, which ensures the flexibility and adaptability of the system. The study also uses self-organization and learning methods to ensure continuous improvement of the systemʼs functioning. The article discusses the requirements for knowledge-based management systems, such as flexibility, adaptability, data and knowledge integration, learning and self-organization, transparency of decisions, and resilience to errors and failures. The findings of the study emphasize the importance of implementing knowledge-based approaches in the management system of complex facilities. This allows to increase the level of adaptability and stability of management, which is critical in todayʼs environment of dynamic development of technology and knowledge. The proposed methods can be applied in various fields, which makes them universal and useful for a wide range of scientific and practical tasks, providing a high level of adaptability, efficiency and stability of complex objects management, taking into account modern requirements and challenges.

The Global Mean First-Passage Time for Degree-Dependent Random Walks in a Class of Fractal Scale-Free Trees

Fractal scale-free structures are widely observed across a range of natural and synthetic systems, such as biological networks, internet architectures, and social networks, providing broad applications in the management of complex systems and the facilitation of dynamic processes. The global mean first-passage time (GMFPT) for random walks on the underlying networks has attracted significant attention as it serves as an important quantitative indicator that can be used in many different fields, such as reaction kinetics, network transport, random search, pathway elaboration, etc. In this study, we first introduce two degree-dependent random walk strategies where the transition probability is depended on the degree of neighbors. Then, we evaluate analytically the GMFPT of two degree-dependent random walk strategies on fractal scale-free tree structures by exploring the connection between first-passage times in degree-dependent random walk strategies and biased random walks on the weighted network. The exact results of the GMFPT for the two degree-dependent random walk strategies are presented and are compared with the GMFPT of the classical unbiased random walk strategy. Our work not only presents a way to evaluate the GMFPT for degree-dependent biased random walk strategies on general networks but also offers valuable insights to enrich the controlling of chemical reactions, network transport, random search, and pathway elaboration.

Alarm management approach for supervision of Green Hydrogen Plants

Amid the increasing significance of renewable energy sources, Green Hydrogen Plants (GHPs) have emerged as pivotal contributors to sustainable energy solutions. However, efficient and reliable alarm management in such complex systems remains a significant challenge. This paper presents a novel methodology called V-nets-based alarm management (VBAM), designed to improve supervision by addressing the intricacies of discrete event management in industrial applications such as GHPs. VBAM offers a powerful formalism that integrates visual modeling and temporal patterns, enabling the precise detection and handling of alarms. The proposed methodology is applied to a case study in a GHP, where critical operational parameters are continuously monitored. By analyzing discrete events and their temporal patterns, V-nets facilitate early fault detection, minimize false positives, and optimize alarm response. The theoretical application of VBAM demonstrates its efficacy in improving system safety, reducing downtime, and enhancing overall operational efficiency within GHPs. The contributions of this work to the state of the art include the development of a comprehensive VBAM methodology tailored to GHPs, as well as the theoretical and practical demonstration of its potential impact on alarm management within GHP operations. The outcomes from the experiments showcase the adaptability and effectiveness of VBAM in addressing the complexities of alarm management in GHPs, thereby contributing to enhanced safety, efficiency, and resilience in the operation of GHPs.

Synthesising the Relationships Between Food Web Structure and Robustness.

For many decades, ecologists have sought to understand the extent to which species losses lead to secondary extinctions-that is, the additional loss of species that occurs when resources or key interactions are lost (i.e. robustness). In particular, ecologists aim to identify generalisable rules that explain which types of food webs are more or less robust to secondary extinctions. Food web structure, or the patterns formed by species and their interactions, has been extensively studied as a potential factor that influences robustness to species loss. We systematically reviewed 28 studies to identify the relationships between food web structure and robustness to species loss and how the conclusions depend on methodological differences. Contrary to popular belief and theory, we found relatively consistent, positive relationships between connectance and robustness, among other generalities. Yet, we also found that conflicting conclusions about structure-robustness relationships can be, in part, attributed to differences in the type of data that studies use, particularly studies that use empirical data versus those generated from theoretical models. This review points towards a need to standardise methodology to answer the open question of whether robustness and its relationship with food web structure and to provide applicable insights for managing complex systems.

DELIBERATIVE DEMOCRACY AND GENDER EQUALITY: CASE STUDY OF THE 2023 WOMEN'S SOCCER CUP IN SOCIAL AUDIT AND CITIZEN TRANSPARENCY

This article aims to analyze deliberative democracy, based on Jürgen Habermas' theory, applying it to the context of public and private entities, with a focus on gender equality in the 2023 Women's Football World Cup. The article will present a replicable model of social auditing, integrating Habermas' communicative rationality with the guidelines of the Brazilian Courts of Auditors. A connection will also be established between communicative rationality and administrative practices for the management of complex systems, since they share foundations of validity, correctness, sincerity, and truthfulness. The questions are based on the reports produced by the international media during the mega-event, using gender inequality as a category. The central argument lies in measuring the permeability of democratic civic common sense and identifying barriers to communication and effective engagement. The article seeks to contribute to citizen education and the integration of theory and practice in the face of the challenges of participatory democracy. The research presents two important results, the first confirms recent research on gender inequality in sport, as media coverage demonstrated disparities in women's football, and the second is the failure to comply with the rules of citizen transparency of sports entities.

Complex system management: Intuition and/or analysis with artificial intelligence involvement

AbstractThis paper tackles the largely untouched issue of optimal management of complex systems, now rendered much more challenging by current conditions of instability and turbulence. Managers need capabilities of foresight and holism in capturing the essence of wicked problems and the best way forward. Interviews were conducted with complex systems managers regarding their perceptions of the value of analysis and intuition in these circumstances and the potential of artificial intelligence (AI) to bring extra value to both modes, analysis, and intuition. Findings indicate that both styles were employed, and descriptions of intuition covered both experience‐based intuition and creative intuition. Regarding the encroachment of AI, the advantages of speed and reduced costs seemed to these respondents to make its adoption mandatory. However, the oversight role of people was considered critical by most respondents. The paper presents a framework to assist managers in understanding the relative roles of AI, analysis, and intuition as these relate to the complexity level of the system being managed.

Guest editorial: Complex system management in mega infrastructure projects

Guest editorial: Complex system management in mega infrastructure projects

A model of knowledge chain integration for complex product R&D projects based on DSM and TCPN

AbstractComplex product research and development projects usually are large scale with high cost and complex technologies and with large number of group members involved. From the perspective of complex system management, this paper adopts design structure matrix and timing constraint Petri nets (TCPN) to analyse knowledge behaviour and time constraints of team members in complex product R&D projects. In specific, this paper first develops a knowledge‐activity design structure matrix and a time‐resource design structure matrix model. Then a TCPN model that integrates resource constraints in knowledge chains is proposed based on the dependence relationship of team members' knowledge activities. Schedulabilities of the model are calculated, analysed and corrected to integrate team members' knowledge. Finally, a case study is conducted to test the proposed model.

Automated generation of digital models for manufacturing systems: The event-centric process mining approach

Digital and simulation models support the design and management of complex systems. However, system modelling is a time-demanding and knowledge-intensive activity. Moreover, modern manufacturing systems are subjected to frequent changes in production plans and subsequent reconfigurations. Therefore, the quick regeneration of the digital models is necessary to align digital twins and cyber-physical systems. This paper proposes a novel event-centric process mining paradigm, a process discovery algorithm, and a set of Key Performance Indicators for the fast and automated generation of digital models and their benchmarking. The discovery algorithm is based on the Event Relationship Graph of the conceptual model of the physical line. The algorithm is tested in four realistic systems of increasing complexity to verify the accuracy in modelling multi-product systems with re-entrant flows and random reworks in the presence of the assembly, disassembly, and split processes beyond the processing operations, and multi-operation workstations. The Event Relationship Graphs of the four systems are presented through the equivalent Petri nets models. The proposed approach is suitable for systems where the sensor positions are known and meaningful, like manufacturing systems, and it is effective for the quick automated generation of digital models for the activities of production planning and control as it requires a few seconds of computation time and a few hours of system observation.

Blockchain-Based Vendor Management in IT: Challenges and Solutions

The rapid evolution of the IT sector has led to increasingly complex vendor management systems, necessitating innovative solutions to handle the multifaceted challenges associated with these systems. Traditional vendor management practices often struggle with issues related to transparency, security, inefficiencies in communication, and trustworthiness among vendors. Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a compelling solution to these challenges. This research paper explores the application of blockchain technology in IT vendor management, focusing on its potential to address critical challenges and enhance the overall efficiency of vendor-related processes. The paper begins by outlining the inherent challenges in conventional vendor management systems, including difficulties in verifying vendor credentials, managing contracts, ensuring data security, and maintaining a reliable audit trail. These challenges often result in operational inefficiencies, increased costs, and potential risks related to vendor fraud or non-compliance. Blockchain technology, known for its secure, transparent, and decentralized nature, offers a transformative approach to these challenges. By providing a distributed ledger that records all transactions in a secure and immutable manner, blockchain can significantly enhance the transparency and security of vendor management processes. The paper examines how blockchain can be used to automate vendor verification, streamline contract management through smart contracts, and ensure data integrity across the vendor lifecycle.

Exploring The Boundaries of Uncertainty: Interval Valued Pythagorean Neutrosophic Set and Their Properties

The interval-valued Pythagorean fuzzy set (IVPFS) presents a novel approach to tackling vagueness and uncertainty, while neutrosophic sets, a broader concept encompassing of fuzzy sets and intuitionistic fuzzy sets, are tailored to depict real-world data characterized by uncertainty, imprecision, inconsistency, and incompleteness. Additionally, the development of Interval Value Neutrosophic Sets (IVNS) enhances precision in handling problems involving a range of numbers within the real unit interval, rather than focusing solely on a single value. However, despite these advancements, there is a deficiency in research addressing practical implementation challenges, conducting comparative analyses with existing methods, and applying these concepts across various fields. This study aims to bridge this research gap by proposing a novel concept based on the Interval Valued Pythagorean Neutrosophic Set (IVPNS), which is a generalization of the IVPFS and INS. The development of IVPNS provides a more comprehensive framework for handling uncertainty, ambiguity, and incomplete information in various fields, leading to more robust decision-making processes, improved problem-solving capabilities, and better management of complex systems. Furthermore, this research introduces the algebraic operations for IVPNS, including addition, multiplication, scalar multiplication, and exponentiation and provides a comparative analysis with IVPFS and IVNS. The study incorporates illustrative numerical examples to demonstrate these operations in practice. Additionally, this study provides and rigorously proves the algebraic properties of IVPNS, specifically discussing their commutative and associative properties. This validation ensures compliance with established conditions for IVPNS, reinforcing their theoretical soundness and practical applicability.

Нарушения на транспорте по Уголовному уложению России 1903 г.

Purpose. Criminal legislation has the task of protecting the state system and social system, their individual structures and institutions. The peculiarity of the Criminal Code of 1903, regarding these tasks, is that it was adopted in an exceptional and special historical period of time. First of all, this circumstance is associated with the period of time that covered the development of criminal legislation during the 19th century, which was reflected in its system, structure and criminal law norms. Secondly, as noted in the preamble of the Criminal Code of 1903, it is adopted in the context of the ongoing state and social transformations, taking into account the requirements of new decisions. Significant changes related to the formation of criminal law provisions providing for liability for violation of the rules of management, use, maintenance and disposal of transport. It should also be noted that the Criminal Code of 1903 represented and was the basis for the development of criminal legislation of the new century, including criminal liability for transport crimes. Methodology: dialectics, abstraction, analysis, synthesis, deduction, — formal legal method, comparative law method. Conclusions. Firstly, the second half of the nineteenth century is characterized by the active construction of the transport system and its infrastructure; secondly, the need arose to form special governing bodies that were not previously provided for in the structure of government bodies of the Russian Empire; thirdly, the safety of traffic and operation of transport required full legal support for the management of the transport system; fourthly, the organization of transport traffic required the formation of institutions and employees for the training of specialized specialists; fifthly, the task of uninterrupted operation of the transport system and the movement of vehicles predetermined the need for systematic maintenance and repair of vehicles and transport infrastructure, sixthly, points/ stations for the formation of trains, loading/reloading of goods and boarding/transfer of passengers deserved to be formed and provided; seventhly, the need arose to form transport teams, determine for them the place and time of meals, rest and interchangeability, and then, as a necessary component of any activity, the function of monitoring the movement of vehicles, especially their technical equipment, was predetermined in order exclusion of accidents. Scientific and practical relevance. To specify the categories of the Criminal Code of 1903 under consideration in order to determine their meaning and place in the current national criminal legislation and in the science of criminal law. Use the results in the preparation of regulations providing for liability for encroachment on the safety of traffic and operation of vehicles, in a complex modern management system in the field of organization and operation of transport, especially in international and related areas, as well as in the educational process when training specialists in the field law enforcement.

Digital Twin of Space Environment: Development, Challenges, Applications, and Future Outlook

This paper explores and discusses the revolutionary applications of digital twin technology in space environments and its profound impact on future space exploration activities. Originating from a proposal by the National Aeronautics and Space Administration (NASA) in 2002, digital twin technology aims to enhance the safety and reliability of space missions by creating precise virtual models. As the technology has evolved, its applications have successfully expanded beyond aerospace to include Industry 4.0, healthcare, and urban management, demonstrating remarkable cross-industry adaptability and broad impact. In space applications, digital twin technology can not only improve spacecraft design and maintenance processes but also enhance the efficiency of mission planning and execution. It plays a crucial role in astronaut training and emergency response as well. Particularly in extreme space conditions, this technology provides real-time monitoring and fault prediction, significantly enhancing mission safety and success rates. However, despite its recognized potential, the implementation of digital twins in space environments faces numerous challenges, including data transmission delays, model accuracy, and the design of user–system interactions. In the future, as artificial intelligence (AI) and machine learning (ML) technologies become mature and integrated, the digital twin will play a more central role in space missions, especially in remote operations, complex system management, and deep space exploration. This article is to overview key technical features, application examples, and challenges of digital twin technology, aiming to provide a comprehensive reference framework for researchers and developers while inspiring further in-depth studies and innovative applications.

Business activity and risks of trading enterprises: the dialectic of the relationship

The paper defines the content of the definition of «business activity» in a broad and narrow sense, the components of its assessment are indicators of logistical, commercial, financial, and investment activities. It is shown that regular assessment and analysis of the business activity of a trading enterprise allows timely detection of problems, optimization of the use of resources, increasing the efficiency of its activity and making informed management decisions. It is shown that risk is an integral part of the economic activity of trading enterprises, which requires special attention and management efforts to ensure the stability and development of enterprises. Approaches to the creation of a complex risk management system with the selection of individual components, the integration of which will ensure the stability and sustainability of trading enterprises in conditions of uncertainty and rapid changes in the market environment, are proposed. A set of measures that will contribute to improving the competitiveness of trade enterprises and increasing their ability to adapt to changes in the economic environment are substantiated. Implementation of the proposed tools will allow trade enterprises to better understand and analyze the needs of their customers, thereby ensuring more efficient work in a risk-oriented environment. The principles of the use of digital technologies in the economy of a commercial enterprise are proposed, including such elements as big data analytics, marketing automation, personalized communications and the integration of various sales channels. This provides the ability to quickly adapt to changes in the market situation, reducing risks and increasing the efficiency of business processes of a trading company. It has been proven that the intensification of business activity and the reduction of risks of trade enterprises require a systematic approach, which includes strategic planning, innovation, effective management of resources and risk minimization. Implementation of the proposed strategies will help trading enterprises to increase their competitiveness, reduce risks and ensure stable development in conditions of market uncertainty.

Unveiling the path to green innovation: the interplay of green learning orientation, knowledge management capability and manufacturing firm’s capability to orchestrate resources

PurposeAmidst the increasing global emphasis on environmental sustainability, manufacturing firms seek to integrate eco-conscious practices into their innovation processes. This study aims to explore the intricate relationships between green learning orientation (GLO), knowledge management capability (KMC), resource orchestration capability (ROC) and two dimensions of green innovation (GI): green product innovation (GPDI) and green process innovation.Design/methodology/approachPartial least squares structural equation modelling (PLS-SEM) and moderated mediation techniques were used to investigate the relationships among the constructs using data gathered from a survey of 167 manufacturing firms in the United Arab Emirates.FindingsThis study indicates that GLO significantly influences GPDI and green process innovation. Although KMC mediates the relationship between GLO and process innovation, it does not mediate the GPDI relationship. Moreover, ROC significantly strengthens the links between GLO, KMC and both the aspects of GI.Practical implicationsThis study emphasises the importance of fostering a green learning culture and integrating it into product development without complex knowledge management systems. This study also highlighted the role of effective resource allocation in maximising environmental learning benefits for sustainable innovation. Organisations can achieve environmental progress by integrating green knowledge into product and process development and by investing in sustainable practices.Originality/valueBy examining various mechanisms involving moderation and mediation, this study has made a notable contribution to advancing the field of knowledge-based view theory. This study also offers enhanced insights into the interconnections among GLO, knowledge management capability, ROC and a firm’s capacity for GI.

Model‐Based Systems Engineering (MBSE) Application in Nuclear Power Plants (NPP)

AbstractCompanies in the nuclear power sector are constantly being challenged to improve their safety and reliability due to increasing complexity arise from evolving safety regulations, long production life, interdisciplinary collaboration, and the need for analyzing the impact of the changes in an operational life cycle. Recognizing these challenges, the paper proposes a transition to Model‐Based Systems Engineering (MBSE) as a transformative solution to improve the management of such complex systems. With this objective, this paper presents a workflow implementation that demonstrates the MBSE methodologies to define a concept model, system architecture, impact analysis, safety and reliability analysis, and operational decision‐making of Nuclear Power Plants (NPP). The paper concludes that MBSE provides a potent approach to managing NPP by employing graphical models to develop interrelated systems that has strong adaptability to heterogeneous environments and regulatory changes. The simulation results demonstrated an NPP life cycle, impact analysis, and a test case for model‐based safety and reliability analysis for regulatory compliance, operational efficiency, balance safety, and informed decision‐making in NPP. The study also leads to a number of interesting directions of future work such as synchronization through Product Lifecycle Management, integration with Building Information Modeling, Model‐Based Commissioning/Decommissioning, and Model‐Based Cyber System Security tailored for nuclear power systems.

Risk Management in Project Planning for Life Science R&D: An Integration of the NTCP Framework

AbstractAs with many industries, the early life science R&D drug discovery sector is facing growing pressure and higher demands on products in terms of cost, quality, and time‐to‐market. Additionally, the complexity of involved targets and systems, requirements for rapid, safe, and developable candidates are increasing. The drug discovery market, often regarded as rather conservative, relies more and more on advanced technologies. It is therefore a significant task for suppliers to create good solutions that meet customer requirements. The life science industry has a long tradition of using projects as the preferred method to manage these complex systems developments, such as the production of target proteins, screening of compounds, and follow‐up of hit compounds. When applying the project approach, the level of uncertainty is usually high, and the risk of those uncertainties must be managed starting in the early planning phase. Thus, this paper focuses on the issue of how to manage risks in the early project planning phase. We first review state‐of‐the‐art practices in risk management for complex systems project management and identify an important framework, Novelty‐Technology‐Cost‐Pace (NTCP), and apply it to successful risk management for early life science projects. Through an in‐depth case study in the life science industry, we demonstrate a systemic integration of the NTPC framework into project planning.

Integrating AI support into a framework for collaborative decision-making (CDM) for airline disruption management

Airlines are regularly confronted with disruptions that interfere with their flight operations, resulting in financial losses and lower operational performance. While collaborative decision-making (CDM) is a commonly used approach to mitigate these airline disruptions, it is unclear how artificial intelligence (AI) can support CDM to manage airline disruption. This study's purpose is to identify how the adoption of AI can support CDM to mitigate operational flight disruptions. Using a theory building approach, this conceptual paper advances the literature in aviation management by delineating the relationship between AI and CDM in the context of airline disruption management. First, we propose an AI–CDM framework illustrating the factors that influence disruption management in airlines. Second, we highlight the implications of AI-supported CDM for disruption management in and for airlines. We found that to effectively use AI-supported CDM for disruption management, airlines need to a) introduce data-driven CDM, b) enable AI management of complex systems, and c) transform disruption management into AI-supported performance management. As one of the first studies linking AI and CDM, the framework provides a structured recognition of the role of AI in CDM and its implications for airline disruption management.

Scenario mapping for critical infrastructure failure under typhoon rainfall: A dependency and causality approach

The critical infrastructure system exhibits a high degree of instability because of the frequent occurrence of extreme natural disasters. This study proposes a method for exploring the cascading failures of critical infrastructure systems under such impacts, based on dependency relationships for importance analysis and probability assessment. With this method, a cascading failure scenario graph of critical infrastructure is established and empirical research is conducted using typhoon rainfall events as a case study. The main contributions of this study are summarized as follows: it proposes a method for the causal identification of the interrelationships among critical infrastructures; it sorts and evaluates the importance and cascading failure probability of critical infrastructure by using the Interpretative Structural Modeling (ISM) and Rank-order Centroid method (ROC); and it generates scenario graphs of critical infrastructure cascading failures to visualize event states, environmental characteristics, and evolutionary paths. This study provides a new method and tool for disaster risk management of complex systems involved in critical infrastructure.

Multilayer analysis of energy networks

Modern interconnected and vulnerable energy infrastructures highlight the critical need for resilience and robustness to maintain the functioning of modern societies amidst growing environmental and operational challenges. This study introduces both multilayer and multiplex network analysis of Belgium’s electricity and gas systems, addressing the complex interdependence inherent in such infrastructure. The resilience is evaluated through random and targeted disruptions, while the robustness is assessed by employing the Molloy–Reed criteria for structural integrity and setting minimum operational thresholds for continuity. Multiplex layer similarities are assessed and subnetworks are identified. The coverage evolution metric quantifies the network’s adaptability under various disruption scenarios. Belgium’s energy infrastructure is used as a case study to uncover critical nodes from different centrality perspectives whose stability is essential for overall network resilience and identify vulnerabilities to disruptions, whether random or targeted. Our insights into the structural robustness, cluster cohesion, and Navigability of energy networks pave the way for more informed decisions in energy infrastructure planning and complex systems management, offering valuable lessons that can be applied globally.