Network Resilience Research Articles

Urban Resilience Index for Critical Infrastructure: A Scenario-Based Approach to Disaster Risk Reduction in Road Networks

Floods pose a significant threat to road networks globally, disrupting transportation, isolating communities, and causing economic losses. This study proposes a four-stage methodology (avoidance, endurance, recovery, and adaptability) to enhance the resilience of road networks. We combine analysis of constructed assets and asset system performance with multiple disaster scenarios (Reactive Flood Response, Proactive Resilience Planning, and Early Warning Systems). Advanced flood Geospatial-AI models and open data sources pinpoint high-risk zones affecting crucial routes. The study investigates how resilient assets and infrastructure scenarios improve outcomes within Urban Resilience Index (CRI) planning, integrating performance metrics with cost–benefit analysis to identify effective and economically viable solutions. A case study on the Lisbon Road network subjected to flood risk analyzes the effectiveness and efficiency of these scenarios, through loss and gain cost analysis. Scenario 2, Proactive Resilience Planning, demonstrates a 7.6% increase compared to Scenario 1, Reactive Flood Response, and a 3.5% increase compared to Scenario 3, Early Warning Systems Implementation. By considering asset performance, risk optimization, and cost, the study supports resilient infrastructure strategies that minimize economic impacts, while enabling communities to withstand and recover from flood events. Integrating performance and cost–benefit analysis ensures the sustainability and feasibility of risk reduction measures.

Sustainable multi-biofuel production with stochastic lead time and optimum energy utilization under flexible manufacturing

Many nations obtain their energy from fossil fuels. The benefit of renewable energy is that it decreases the dependence on conventional fuel, and minimizes carbon emissions. However, the commercial feasibility of multi-biofuel production poses a major challenge. A resilient and cost-efficient biofuel supply chain network is essential for commercialization. Furthermore, disruption risks arise from operational downtime, labor strikes, natural disasters, and uncertainty embedded in the data compromise the effectiveness of tactical and strategic level supply chain planning. In line with the aforementioned requirements, a multi-biofuel supply chain network model that reduces the total system cost and accounts for both disruption and operational risks is proposed. The proposed model determines the optimal production–distribution quantities and supports facility location and capacity decisions against multiple supply and demand interruption scenarios. The lead time crashing cost is utilized here to reduce the lead time, which increases because of transportation disruptions. To decrease energy expenses and carbon emissions, a variable production rate is applied for creating an economic way to advance the manufacturing industry. The model aims to show bioenergy’s effect on making a sustainable multi-biofuel supply chain network. Two numerical examples are used to illustrate the effectiveness of the proposed model. It is seen that by utilizing the presented method, the production cost of biofuels is significantly reduced through minimized energy consumption and carbon emissions. Moreover, the impact of critical parameters on the total system cost is explained through sensitivity analysis and graphical representations. The effort provides a systematic guideline for designing various environmental, economic, and social aspects of a sustainable supply chain network for bioenergy under minimized consumption. Multi-biofuel producers, distributors, investors, and the regulators surely be benefitted from the proposed model.

A Proposal of Resilient Supply Chain Network Planning Method with Two-stage Stochastic Programming and Latin Hypercube Sampling

A Proposal of Resilient Supply Chain Network Planning Method with Two-stage Stochastic Programming and Latin Hypercube Sampling

A case study on scenario-based optimization for axle load survey data selection for flexible pavement design in Vietnam

The unique traffic patterns in Vietnam, characterized by a mix of vehicle types, axle configurations, and overloading, pose challenges to current flexible pavement design methods. This study investigates how selecting different vehicle axle load survey data scenarios impacts the accuracy of pavement design and rehabilitation for roads in Vietnam. Evaluation of various data selection methods and their influence on pavement response using the current flexible pavement calculation procedure in Vietnam. By comparing the pavement design criteria calculated under each scenario, this research aims to provide clear guidelines for engineers choosing appropriate axle load data for pavement design. This, in turn, will lead to the design and maintenance of more durable and sustainable pavements, ultimately promoting a more efficient and resilient transportation infrastructure in Vietnam. The research approach involves analyzing various axle load survey data scenarios, including those representing typical traffic conditions, overloaded vehicles, and specific vehicle types. The calculated pavement responses under each scenario are then compared to assess the impact of data selection on design outcomes. This study's findings are expected to provide valuable insights for pavement engineers in Vietnam, enabling them to select appropriate axle load data for accurate pavement design and rehabilitation. This will contribute to building more durable and sustainable road infrastructure, resulting in a more efficient and resilient transportation network

Unraveling the Hydration Shell Structure and Dynamics of Group 10 Aqua Ions.

We present ab initio simulations based on subsystem DFT of group 10 aqua ions accurately compared against experimental data on hydration structure. Our simulations provide insights into the molecular structures and dynamics of hydration shells, offering recalibrated interpretations of experimental results. We observe a soft, but distinct second hydration shell in Palladium (Pd) due to a balance between thermal fluctuations, metal-water interactions, and hydrogen bonding. Nickel (Ni) and platinum (Pt) exhibit more rigid hydration shells. Notably, our simulations align with experimental findings for Pd, showing axial hydration marked by a broad peak at about 3 Å in the Pd-O radial distribution function, revising the previously sharp "mesoshell" prediction. We introduce the "hydrogen bond dome" concept to describe a resilient network of hydrogen-bonded water molecules around the metal, which plays a critical role in the axial hydration dynamics.

Capacity, resilience and virtual embedding in elastic optical networks planning with adopted machine learning

Capacity, resilience and virtual embedding in elastic optical networks planning with adopted machine learning

Liner shipping network vulnerability to component disruptions: A China-Europe container flow analysis

This study conducts a vulnerability analysis of the China-Europe liner shipping network, including all relevant liner shipping services and China Railway Express services. The standard multicommodity network flow model is extended to assess network vulnerability and the relative importance of ports and services by simulating container flow redistribution in various disruption scenarios. The results show the network is highly resilient to both individual port disruptions and individual service disruptions. Additionally, the distinct shipping network structures of the three shipping alliances result in varying degrees of vulnerability, highlighting the significance of diversified market suppliers in enhancing the overall network resilience. Specifically, the Ocean Alliance is vital in facilitating China-Europe trade, especially along the Mediterranean route, and is more resilient to individual port disruptions.

An Integrated Infrastructure Resilience Approach, from the Geotechnical Asset to the Transport Network

The main objective of this work is to present a comprehensive and integrated methodology to enhance the resilience of transportation critical infrastructure (TCI), focusing on the interplay between geotechnical assets and the transport network. Societies are greatly dependent on transport infrastructure systems, and as the mobility of passengers and the transport of freight is continuously growing, a disruption due to natural or man-made hazards creates significant impacts and dysfunctionalities on their operation and necessitates response measures to minimize vulnerability and ensure continuous functionality and robustness through resilience. Therefore, resilience quantification allows the design of ad hoc operation action plans before, during, and after a disruption, considering the dynamics of societal, ecological, and technological (SET) environments. The current work focuses on resilience quantification methodologies for TCIs and on the influence of single geotechnical asset (i.e., slope failure) resilience capacity on the overall system (i.e., national road network) resilience. Two case studies of unexpected transport network disruptions that took place in Greece are presented, and resilience metrics and performance indicators are applied to quantify the influence of the recovery stage.

Temperature Fluctuations in Power conductors: Analysis and Overcurrent Relay Adaptation Strategies for Mitigation

In electrical networks, the efficient flow of energy faces constraints primarily associated with the conductor’s temperature. By assessing transmission infrastructure, identifying critical geographical and climatic zones, one can compute the line’s temperature. Focusing on the importance of maintaining optimal conductor temperature for efficient power transmission, this paper evaluates the critical role of conductor temperature in overhead transmission lines and explores its dynamic variations influenced by various ambient conditions. Emphasizing the significance of maintaining optimal conductor temperature, the study delves into mitigation strategies and adaptive protection mechanism, with a focus on overcurrent relays, with specific emphasis on the efficacy of current-time graphs. By addressing these factors, the research aims to enhance the reliability and efficiency of power transmission systems, ensuring sustained performance under diverse environmental scenarios. Overall, the paper contributes valuable insights to optimize the flow of energy in overhead transmission lines, thereby enhance the robustness also, improving the resilience and functionality of electrical networks.

Comprehensive Evaluation of Resilience for Qinling Tunnel Group Operation Safety System Based on Combined Weighting and Cloud Model

An extensive network of tunnels has recently been constructed in the Qinling Mountains. Characterized by high and steep terrain, this network has led to frequent traffic accidents. To address this issue, this paper introduces the theory of resilience into the evaluation system of safety systems during the operation period of highway tunnel groups. Based on this, this paper establishes a resilience evaluation index system for the operation safety system of highway tunnel groups, including a human system, vehicle system, and road system. To address both qualitative and quantitative issues concerning the indicators, this paper employs the analytic hierarchy process (AHP) and entropy weight method to combine and assign weights to the resilience evaluation indicators. Subsequently, the cloud model method is utilized to quantify the level of resilience of the highway tunnel group safety system during the operation period. The study results unveiled the patterns of traffic accidents within the Qinling Tunnel Group from the perspectives of vehicle, road, and human factors. The final weight allocation reveals that the road system has the highest proportion, exerting the greatest influence as a primary level index. Moreover, by taking the Qinling Tunnel Group on the Xihan Expressway as an engineering example, the resilience level of the case project was analyzed and obtained. Proposals for enhancing resilience were put forth, taking into account the project’s unique attributes, encompassing adaptability, resistance, and recovery. Overall, this study validates the feasibility and reliability of the proposed method for assessing the resilience of highway networks, offering empirical support for transportation administrators in the implementation of resilience-enhancing strategies.

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.

Integrated relief pre-positioning and procurement planning considering non-governmental organizations support and perishable relief items in a humanitarian supply chain network

The escalating frequency and severity of disasters on a global scale have sparked inquiries into the efficacy of current disaster planning strategies in various scenarios. Despite the pivotal role of humanitarian supply chain planning in aiding impacted populations, much of the existing research is grounded in simplistic assumptions that limit their practicality. Addressing this gap, our proposed bi-objective model aligns response time and total cost, while also accommodating the collaboration between non-governmental organizations and governmental organizations to mirror real-world intricacies. This study comprehensively delves into various logistics aspects, encompassing pre- and post-disaster phases, including location, allocation, supplier selection, fleet size, supply contract, inventory, distribution, and transportation. This multifaceted approach enhances the model's suitability for managing genuine real-world emergencies. To mitigate disruption risks and unforeseen events, the model introduces pre-positioning, quantity flexibility contract, backup suppliers, and a multi-sourcing policy, thus enhancing the resilience and reliability of the logistics network. We present solutions for diverse scenarios through a scaled weighted sum method, while tackling uncertainty via a heuristic approach known as the backward scenario reduction method. Furthermore, to manage large-scale problems within an acceptable time frame, we propose an advanced hybrid algorithm. This algorithm synergizes a parallel differential evolution framework with reinforcement learning-enhanced local search mechanisms, aiming to improve both computational efficiency and solution accuracy. Finally, we validate the model's applicability through a real case study focusing on a flood scenario in Iran.

Exploring factors influencing the entrepreneurial intentions of the youth community towards green ICT to encourage environmental sustainability: Evidence from an emerging economy

AbstractInformation and communication technologies (ICTs) have a complex relationship with the environment, with certain positive as well as negative effects. Through a comprehensive examination of the entrepreneurial landscape in the domain of green ICT in Uttar Pradesh, India, the research aims to investigate the factors influencing the entrepreneurial intentions of the youth community towards green ICT to encourage environmental sustainability. The research shed light on the involved interplay of individual characteristics, social and environmental contexts, and educational and institutional support systems. By employing a comprehensive qualitative study‐interview approach combined with exploratory factor analysis (EFA) various factors such as technological innovation, supportive regulatory frameworks (government policies), entrepreneurial resilience, family and social network support, environmental awareness and concern, availability of resources, market demand, and support of academic institutions are identified as the prominent factors which underscore the multifaceted nature of entrepreneurial intentions among the youth community. The findings contribute valuable insights for policymakers, educators, and entrepreneurs alike, emphasizing the need for targeted interventions and policies to nurture a vibrant ecosystem of green ICT entrepreneurship, thereby driving sustainable development and environmental conservation.

Exploring R&D network resilience under risk propagation: An organizational learning perspective

Through the development of a risk propagation model, this paper investigates R&D network resilience under risk propagation from an organizational learning perspective. Drawing from organizational learning theory, we first quantify failure experience in the context of risk propagation. Then a risk propagation model is developed, which is composed of two parts: spontaneous mechanism and propagation mechanism. Finally, we implement agent-based simulation to show how organizational learning and forgetting impact on R&D network resilience and firm vulnerability under risk propagation. Our results show a significant association between organizational learning and both network resilience and firm vulnerability. However, the results differ in different levels of risk. When the propagation probability is low, network resilience can be enhanced through improving the learning ability or decreasing forgetting for both high-vulnerable and low-vulnerable firms. However, in other scenarios, the low-vulnerable firms are more critical to network resilience. Additionally, the impact of firm failure magnitude on enhancing network resilience will gradually decrease with the increase of firm learning rate. Our results will help practitioners understand the effect of organizational learning on R&D network resilience and support decision-making in risk management.

Resilience as a Key Factor in the Success of Women-Led Entrepreneurship: A Systematic Literature Review

Resilience in female entrepreneurship emerges as a critical component that significantly influences the achievement and capacity to overcome challenges inherent in the creation and management of businesses. To address this topic, the present article focused on conducting a systematic review of the scientific literature to examine how resilience stands as a determining factor enabling female entrepreneurs to navigate obstacles and achieve success in their ventures. In line with this objective, a bibliographic methodology was implemented that included synthesizing information from various databases, including Scopus, Web of Science, and SciELO. The results reveal a scarcity of publications on this particular topic, thus highlighting the importance of addressing these gaps in the literature. This approach is essential for gaining a more holistic understanding of the relationship between resilience and female entrepreneurship in various contexts. In this sense, the 152 articles analyzed during the research constitute a valuable starting point for future investigations, evidencing the need to delve deeper into the analysis of resilience in the context of female entrepreneurship. Identifying and understanding these gaps in the scientific literature not only enriches the existing knowledge but also guides new research directions that contribute to the advancement and consolidation of this field of study. Consequently, resilience in female entrepreneurship not only acts as a driver for overcoming challenges but also plays a crucial role in building a more equitable and diverse business environment. The scientific evidence found during the systematic literature review is considered an essential component of promoting gender equality and diversity in the business sphere. In the abovementioned review, the relevance of factors such as resilience, self-efficacy, and support networks in the realm of female entrepreneurship is highlighted. These studies provide valuable perspectives that not only enrich existing knowledge but can also guide the development of policies and practices aimed at creating a more inclusive and equitable environment for female entrepreneurs. The interconnection between these elements underscores the importance of considering not only individual resilience but also the impact of external factors and support in fostering a more favorable business ecosystem for women. Received: 17 January 2024 / Accepted: 8 April 2024 / Published: 5 May 2024

Fault Identification in Optical Transport Network Using CNN

The growing diversity Regarding the provision of information and services transmitted Using optical technology transport networks (OTN) has made network survival a crucial issue in current study. Fault detection refers to the process of identifying faults that arise from various issues such as packet loss, disconnection, and others in the OTN. The determination of the fault location in an OTN is highly significant in the analysis of the resilience of optical networks. This study presents a fault diagnosis system that utilises a Convolutional Neural Network (CNN), focusing primarily on distinguishing between hard faults (HF) and soft faults (SF). The CNN is implemented in where the fault is situated domain of OTN to determine the presence or absence of potential fault locations. The notion An F-Measure is implemented in order to quantify the impact of positioning.utilising Location, time, mean squared error (MSE), and F-measure. The scientific investigation demonstrates that the suggested CNN neural network achieves the highest performance. The suggested CNN has a reduced localization time and achieves an F1-score of 0.98 after 85 iterations. This level of accuracy and real-time performance fulfils the requirements for fault identification. Hence, there is significant potential and practical utility in incorporating neural networks in the field of identifying and locating faults in optical transport networks.

A Resilient Marine Protected Area Network Design: A First for the Arafura Timor Seas

The Arafura and Timor Seas (ATS) comprise both near pristine and highly threatened marine ecosystems. There are four littoral nations (Indonesia, Timor-Leste, Australia, and Papua New Guinea), which have independently established Marine Protected Areas (MPAs). We design the first resilient MPA network for the entire region to protect critical habitats and species by considering transboundary features (i.e., connectivity of fisheries, rare, threatened, and protected species), uses and threats (i.e., fishing and climate change). We used Geographic Information System processing to identify gaps in the regional network based on existing and proposed MPAs and Areas of Interest (AOIs: previously identified as potential new MPAs) in each country, followed by systematic conservation planning to identify new AOIs for establishing MPAs to fill these gaps. The ATS MPA Network design (301,055 km2) has been endorsed by all four countries and includes all 93 existing (265,405 km2) and proposed (6,264 km2) MPAs, 13 previously identified AOIs (14,773 km2), and seven new AOIs identified in this study (14,613 km2). Of the new AOIs, three have been incorporated in Indonesia’s national MPA network plan, one is in a new MPA currently being developed in Timor-Leste, and Australia is considering two as potential areas for new MPAs.

Impact of extreme rainfall on metro network resilience: Shanghai metro as an example

Extreme rainfall results in urban floods that are known to occur frequently and worldwide. In particular, a metro system faces a high risk of inundation owing to its special geographical location. Consequently, metro network flood resilience has attracted increased attention. This study considered Shanghai as an example to assess the resilience of metro network under extreme rainfall. First, a resilience assessment method of metro networks under extreme rainfall conditions was proposed. This method comprised comprehensive urban waterlogging analysis, construction of a topological structure of metro networks, and resilience assessment of metro networks. Subsequently, the resilience analysis of the Shanghai metro network under extreme rainfall was conducted as a case study based on the above method. The results showed that the disruption of stations and corresponding intervals caused greater damage to network performance. Finally, based on the analysis results, suggestions were provided to improve the flood resilience of the metro network under extreme rainfall conditions.

Enhancing reclaimed water distribution network resilience with cost-effective meshing

Water Distribution Networks (WDNs) are critical infrastructures that ensure a continuous supply of safe water to homes. In the face of challenges, like water scarcity, establishing resilient networks is imperative, especially in regions vulnerable to water crises. This study evaluates the resilience of network designs through graph theory, including its hydraulic feasibility using EPANET software, an aspect often overlooked. Novel mathematical algorithms, including Resilience by Design (RbD) and Resilience-strengthening (RS) algorithms, provide cost-effective and resilient network designs, even with budget constraints. A novel metric, Water Availability (WA), is introduced to offer a comprehensive measure of network resilience, thereby addressing ongoing discrepancies in resilience evaluation methods. Practical benefits are illustrated through a case study in which a resilient-by-design reclaimed water network is created, and an existing equivalent non-resilient network is improved. The resilient-by-design network demonstrates remarkably better results compared to the equivalent non-resilient design, including up to a 36 % reduction in the probability of service disruptions and a nearly 65 % decrease in the annual average unserved water due to service disruptions. These findings underscore the enormous advantages of a resilience-focused network design approach. When compared to the equivalent non-resilient design, the resilient-by-design network generated effectively safeguards up to a significant 91,700m3 of water from the impacts of water disruption events over a 50-year operational period. In addition, the resilient-by-design WDN solution incurs a subtle decrease in overall costs compared to consuming tap water from the drinking WDN baseline over a 50-year operational period. These findings highlight the cost-effectiveness of the approach, even offering financial benefits. This paper builds on our previous research by expanding its scope to include resilience considerations, providing algorithms that can be easily adapted from reclaimed to drinking WDNs. Ultimately, we contribute to the enhancement of water resource management and infrastructure planning in ever-evolving urban environments.

ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN OPTIMIZING INVENTORY MANAGEMENT ACROSS GLOBAL INDUSTRIAL MANUFACTURING & SUPPLY CHAIN: A MULTI-COUNTRY REVIEW

This study examines the impact of Artificial Intelligence (AI) and Machine Learning (ML) on inventory management within global industrial manufacturing and supply chains, particularly in the context of Industry 4.0. Through a comparative analysis across several countries, the research analyzes quantitative and qualitative data to assess the adoption and integration of these technologies and their implications for supply chain optimization. The research methodology includes a comprehensive literature review using multiple databases and expert interviews conducted within a specific timeframe. The study identifies a significant favorable influence of AI and ML on enhancing efficiency, reducing costs, and improving real-time data analytics and predictive maintenance. It highlights the evolution from theoretical potential to practical applications, with an increased focus on regulatory compliance and data integrity, reflecting the industry's maturation in digital integration. Furthermore, the study explores the strategic role of AI and ML in process design and the holistic adoption of Industry 4.0 principles across the supply chain. The findings contribute to the academic literature by detailing the benefits and challenges of AI and ML implementation, offering insights for future research and practical applications in the supply chain sector. The conclusion emphasizes the transformative potential of AI and ML, advocating for their strategic implementation to foster resilience and adaptability in supply chain networks.