Unexpected Disruptions Research Articles

Using emerging technologies to improve the sustainability and resilience of supply chains in a fuzzy environment in the context of COVID-19.

In rapidly changing business conditions, it has become extremely important to ensure the sustainability of supply chains and further improve the resiliency to those events, such as COVID-19, that can cause unexpected disruptions in the value supply chain. Although globalized supply chains have already been criticized for lack of control over sustainability and resilience of supply chain operations, these issues have become more prevalent in the uncertain environment driven by COVID-19. The use of emerging technologies such as blockchain, Industry 4.0 analytics model and artificial intelligence driven methods are aimed at increasing the sustainability and resilience of supply chains, especially in an uncertain environment. In this context, this research aims to identify the problematic areas encountered in building a resilient and sustainable supply chain in the pre-COVID-19 era and during COVID-19, and to offer solutions to those problematic areas tackled by an appropriate emerging technology. This research has been contextualized in the automotive industry; this industry has a complex supply chain structure and is one of the sectors most affected by COVID-19. Based on the findings, the most important problematic areas encountered in SSCM pre-COVID-19 are determined as supply chain traceability, demand planning and production management as well as purchasing process planning based on cause and effect groups. The most important issues to be addressed during COVID-19 are top management support, purchasing process planning and supply chain traceability, respectively.

Resilient airline scheduling to minimize delay risks

Airlines tend to design their flights schedules with the primary concern of the minimization of operational costs. However, the recently emerging idea of resilient scheduling defined as staying operational in case of unexpected disruptions and adaptability should be of great importance for airlines as well due to the high opportunity costs caused by the flight cancellations and passenger inconvenience caused by delays in the schedule. In this study, we integrate resilient airline schedule design, aircraft routing and fleet assignment problems with uncertain non-cruise times and controllable cruise times. We follow a data-driven method to estimate flight delay probabilities to calculate the airport congestion coefficients required for the probability distributions of non-cruise time random variables. We formulate the problem as a bi-criteria nonlinear mixed integer mathematical model with chance constraints. The nonlinearity caused by the fuel consumption and CO2 emission function associated with the controllable cruise times in our first objective is handled by second order conic inequalities. We minimize the total absolute deviation of the aircraft path variability’s from the average in our second objective to generate balanced schedules in terms of resilience. We compare the recovery performances of our proposed schedules to the minimum cost schedules by a scenario-based posterior analysis.

Resilience-based approach to maintenance asset and operational cost planning

Reliability-based and risk-based methods for directing maintenance activities play a critical role in ensuring system safety and reducing unnecessary downtime. Those methods focus on preventive maintenance to avoid component failures and are applicable before unexpected disruptions occur. However, when disruptions are unavoidable, more attention should be paid to systems’ recovery from unwanted changes. As a remedy of preventive maintenance, improving system restoration capacity of resilience through optimizing the system's maintenance asset and operational cost is an efficient way to help system restore from disruption conditions within an optimal cost. In this paper, a resilience-based approach is proposed to optimize maintenance asset and operational cost. A novel resilience metric is developed and utilized to quantify system resilience under various restoration capacities. The minimal acceptable resilience level (MARL) and maximal acceptable restoration time (MART) are proposed to determine the optimal maintenance cost. The proposed approach is applied to the Chevron Richmond refinery crude unit and its upstream process. The results show that it can help practitioners identify the optimal cost to ensure a system is resilient to respond to uncertain disruptions and provide a dynamic resilience profile to support decision-making.

Virtual Canine Comfort: A Randomized Controlled Trial of the Effects of a Canine-Assisted Intervention Supporting Undergraduate Wellbeing

ABSTRACT Being a university student is known to be a stressful experience, and researchers across studies have reported elevated stress and compromised mental health for university students when compared with findings from the general population. The unexpected disruption to their studies arising from COVID-19 and its corresponding social isolation added to the stress experienced by students. This study assessed the effects of a virtual canine-assisted intervention to support student stress reduction and wellbeing. Participants (n = 467) were recruited from undergraduate psychology classes at a mid-size Western Canadian university and were randomly assigned to either synchronous (i.e., live Zoom) or asynchronous (i.e., pre-recorded YouTube videos) sessions with or without a dog present. An abbreviated, small group dose intervention of five minutes was used and handlers across conditions followed a script that mirrored as closely as possible the dialogue shared during a typical live, in-person visit (i.e., shared information about their dog, asked participants to reflect on their wellbeing, etc.). Measures of wellbeing included 1-item measures of connectedness to campus, loneliness, and stress, the PANAS, State-Trait Anxiety Inventory, Circumplex Model of Affect, and self-ratings of engagement during the intervention. Results of paired samples t-tests revealed that participants, regardless of condition, reported significant reductions in anxiety, stress, loneliness, and negative affect and more positive emotional states and stronger feelings of connectedness to their campus. Regarding the impact of platform delivery, participants reported greater campus connectedness following their participation in the synchronous conditions; however, type of platform was not associated with other aspects of wellbeing as noted above. As hypothesized, undergraduate students in this study did report feeling less stressed at the end of the intervention when a dog was present. The implications of the findings are discussed within the context of university student wellbeing.

Uncertainties in Liner Shipping and Ship Schedule Recovery: A State-of-the-Art Review

Each shipping line is expected to establish a reliable operating model, and the design of ship schedules is a key operational consideration. Long-term profits for shipping lines can be expected from a well-designed ship schedule. In today’s liner service design, managing the time factor is critical. Shipping schedules are prone to different unexpected disruptions. Such disruptions would necessitate a near-real-time analysis of port capacity and re-design of the original ship schedule to offset the negative externalities. Ship schedule recovery strategies should be implemented to mitigate the effects caused by disruptions at ports or at sea, which may include, but are not limited to, ship sailing speed adjustment, handling rate adjustment at ports, port skipping, and port skipping with container diversion. A proper selection of ship schedule recovery strategies is expected to minimize deviations from the original ship schedule and reduce delays in the delivery of cargoes to the destination ports. This article offers a thorough review of the current liner shipping research primarily focusing on two major themes: (1) uncertainties in liner shipping operations; and (2) ship schedule recovery in response to disruptive events. On the basis of a detailed review of the available literature, the obtained results are carefully investigated, and limitations in the current state-of-the-art are determined for every group of studies. Furthermore, representative mathematical models are provided that could be further used in future research efforts dealing with uncertainties in liner shipping and ship schedule recovery. Last but not least, a few prospective research avenues are suggested for further investigation.

Autism and COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has widened existing inequities faced by autistic individuals. The unprecedented medical, social, educational, and economic upheaval has highlighted the diverse physical health needs of autistic individuals and the importance of addressing these with a life-course approach using multisectoral and policy solutions driven by advocates and self-advocates in the community.Autistic individuals in congregate and group settings, with co-occurring medical conditions are at higher risk for contracting COVID-19 and poor health outcomes.1,2 Wide variations in state vaccine prioritization plans exist, where high-risk disabled populations are not considered high priority.3 Access to routine medical visits has been disrupted during surges in cases, leading to potential delays in accessing necessary diagnoses, treatments and services.4 Emergency preparedness plans often overlook the needs of autistic individuals; for example, the use of the frailty scale to ration care, which unfairly disadvantages autistic individuals.1Social isolation has negative effects on the well-being of autistic individuals who have lost their routine social interactions and support.1 In children, loneliness has been associated with challenging behaviors, worsening mental health, and long-lasting effects into adulthood.5The disruption to learning has been particularly concerning for children with special educational needs.6 Families have reported greater difficulty accessing therapies and services for autistic children and regression in previously attained skills.7 Because of lack of existing standards, the quality of remote therapies and learning is highly variable. Economically disadvantaged children face an additional burden of poor access to technology and stable internet.6 Several autistic children and adults have consistently advocated for availability of virtual learning platforms but have only just benefitted now that these are widely available.1Autistic adults and their families are vulnerable to economic instability and the downstream health implications brought about by the pandemic. Even before the pandemic, autistic adults had the lowest rate of employment among those with disabilities.8 Due to remote schooling during the pandemic, parents may have to forgo their employment to care for their children.1The COVID-19 pandemic has highlighted areas that need urgent attention in the community. Autistic individuals, particularly those at high-risk for COVID-19-related hospitalizations and deaths, should be prioritized to receive the COVID-19 vaccine.9 In promoting equitable access to the vaccine, the focus must be on dissemination of accurate and timely information, creating easily navigable systems and ensuring vaccine confidence. Autistic individuals must be represented in infection control and emergency preparedness planning at multiple levels: for example, within schools, health care settings, residential facilities, etc. Prolonged and unexpected disruptions to health, educational, and behavioral service deliveries during occurrences such as the COVID-19 pandemic must be met with innovative solutions to maximize individual life-course trajectories. Leveraging the virtual environment may be beneficial to some (eg, telehealth and remote learning), while technological innovations (eg, mRNA vaccines and point-of-care testing) could safeguard in-person service delivery for others. Financial stability of affected individuals should be addressed through the provision of economic relief packages, uninterrupted health care and/or unemployment insurance. It is imperative we take immediate action to accommodate autistic children and adults at all levels in our society and close existing inequities.

An Innovative Risk Matrix Model for Warehousing Productivity Performance

In today’s era of industrial economics, warehousing is a complex process with many moving parts and is required to contribute productively to the success of supply chain management. Therefore, risk management in warehouses is a crucial point of contention to ensure sustainability with global supply chain processes to accommodate good productivity performance. Therefore, this study aims to analyse risks factors that affect warehouse productivity performance towards a systematic identification of critical factors that managers should target to sustain and grow warehouse productivity. This study utilised a traditional risk matrix framework, integrating it with the Borda method and Analytical Hierarchy Process (AHP) technique to produce an innovative risk matrix model. The results indicate that from the constructed ten warehouse operation risk categories and 32 risk factors, seven risk categories, namely operational, human, market, resource, financial, security and regulatory, including 13 risk factors were prioritised as the most critical risks impacting warehouse productivity performance. The developed risks analysis model guides warehouse managers in targeting critical risks factors that have a higher influence on warehouse productivity performance. This would be extremely helpful for companies with limited resources but seek productivity improvement and risks mitigation. Considering the increasing interest in sustainable development goals (economic, environmental, and social), arguably, this work support managers in boosting these goals within their organisation. This study is expected to benefit warehouse managers in understanding how to manage risk, handle unexpected disruptions, and improve performance in ever-changing uncertain business environments. It often has a profound effect on the productivity level of an organisation. This study proposes an innovative risks analysis model that aims to analyse risks, frame them, and rate them according to their importance, particularly for warehousing productivity performance.

The impact of COVID-19 on doctoral research: adapting to the challenges and the opportunities of disruption.

The author is a professional doctorate student and full-time nurse in an education role in a UK NHS Trust. COVID-19 caused a major disruption to her research, which focused on nursing workforce development in primary care. She received ethical approval for the research in February 2020 and had just begun to collect data as the country entered its first period of lockdown and all activity not related directly to COVID-19 was sidelined. To share the author's reflections on this experience and make recommendations that may help others whose research is disrupted. The author had not considered in her plans the disruption that would be caused by COVID-19, but she adapted her study's design to minimise some of its effects and also discovered some new opportunities. Nurse researchers whose studies are disrupted can adapt to overcome the challenges and try to find new opportunities. The author hopes that sharing her reflections will encourage other nurse researchers who face unexpected disruptions to their research.

Perspectives on How 1.5 Years of the COVID-19 Pandemic Have Impacted Biophysicists at Primarily Undergraduate Institutions

Perspectives on How 1.5 Years of the COVID-19 Pandemic Have Impacted Biophysicists at Primarily Undergraduate Institutions

Identification of critical stations in a Metro System: A substitute complex network analysis

Metro systems are critical public transport elements in several metropolitan areas around the world. Unexpected disruptions may undermine service provision of metro systems, and thus addressing their negative impacts is of primary importance. A first step towards developing mitigation measures involves the identification of those critical metro stations, whose operation must be preserved. Complex Network Theory (CNT) provides valuable methodological tools for this purpose, as a topological analysis based on centrality measures combined with real-world spatiotemporal data can be used for critical station identification. The objective of this paper is to develop a measure for evaluating metro station criticality based on CNT, considering substitute services during a disruption. A substitute network is defined as the network consisting of the metro stations as nodes and all alternative public transport routes potentially serving those stations outside the metro system, as edges. The form of the substitute network depends on a pre-selected service level. Two graphs are constructed, the metro and the substitute, using an L-space and a P-space representation, respectively. A combination of centrality measures of both networks is utilized for evaluating the stations’ criticality. The methodology proposed is applied to a real-world metro system, that of Athens, Greece. A sensitivity analysis is conducted suggesting that the proposed measures manage to capture the tradeoff between centrality and availability of alternatives, considering a station’s topological criticality. On top of that, the criticality measure seems to be robust against changes at service levels, but sensitive enough, so that it can be adaptable to each operator’s needs. The methodology proposed can be utilized for identifying critical metro stations a priori and thus achieving a more efficient planning, considering metro disruptions.

Assessing production fulfillment time risk: application to pandemic-related health equipment

Manufacturing companies strive to identify and manage the effects of unexpected disruptions (risks) on their production processes, which affect their performance and resilience. In this study, we propose a decision framework to capture the impact of interconnected risk sources, on the efficiency of manufacturing companies. The proposed framework utilises a novel mixed-integer linear programming (MILP) model to minimize the time of satisfying the orders while it considers the risk associated with suppliers and manufacturers. The MILP model considers the relationships among (i) material and suppliers and (ii) work centers to measure the propagation of risks throughout the production system. The proposed framework also utilises the Monte Carlo simulation to calculate the associated likelihood of delay and the distribution of the delivery time of orders. To show the complication of the propagation of risk, two distinct scenarios are compared. The first scenario considers zero risks, while the second one assigns probabilistic risk to the suppliers and work centers. The results highlight the magnitude and the complexity of the risk propagation from various interconnected sources through the production system. It also identifies the most vulnerable components of the production system affected more by various types of risk.

Vulnerability analysis of the Chinese coupled aviation and high-speed railway network

The integrated aviation and High-Speed Railway (HSR) transportation system plays a vital role for today’s inter-city transportation services. However, an increasing number of unexpected disruptions (such as operation failures, natural disasters, or intentional attacks) pose a considerable threat to the normal operation of the system, especially on ground transfer, leading to the extensive research on its vulnerability. Previous approaches mainly focus on interruptions within a single transportation mode, neglecting the role of ground transfer which serves as a coupled connection between aviation and High-Speed Railway. This paper proposes a network-based framework for evaluating the vulnerability of the Chinese Coupled Aviation and High-Speed Railway (CAHSR) network from the viewpoint of ground transfer interruption. Taking the end-to-end travel time and passenger flow information into consideration as an evaluation measure and analyzing from the perspective of urban agglomerations, an adaptive method is developed to identify the critical cities and further investigate their failure impacts on the geographic distribution of vulnerability. In addition, the proposed model explores variations of vulnerability under different failure time intervals. Based on the empirical study, some major conclusions are highlighted as follows: (A) Only a few cities show significant impacts on the network’s vulnerability when ground transfer interruptions occurred. (B) The distribution of vulnerability is not proportional to the distance between failure city and influenced city. (C) The vulnerability is more serious in the morning and evening when the ground transfer is disconnected. Our findings may provide new insights for maintenance and optimization of the CAHSR network and other real-world transportation networks.

Business Resilience System Integrated Artificial Intelligence System

By definition, “Business Resilience” is the ability for an organization to quickly adapt to an unexpected disruption(s) and prevent any ongoing workflow(s) to come to a halt and yet maintaining continuous business operations and safeguarding people, resources, assets, and overall barns equity. By the same talking, a Business Resilience System (BRS) is a combination of intelligent software and hardware combined in an integrated system. Such an integrated combination of Business Resilience System goes a step beyond Disaster Recovery (DR) by offering post-disaster strategies to avoid costly downtime, shore up vulnerability and maintain business operations in the face of additional, unexpected breaches of the daily operation of workflow in any enterprise or organization. With recent technical progress in Artificial Intelligence (AI) augmented with Machine Learning (ML) and Deep Learning sub-systems, they present an Artificial Intelligence System (AIS) and now integrating these two systems of BRS and AIS, one can offer the most intelligent system that an organization or an enterprise can own, in order to have the best possible solution in place to have the best possible technique of predication and consequently prevention and adverse events based on collective historical data within Deep Learning of Artificial Intelligence. In this paper we are present and introduce each of these systems i.e., BRS and AIS and how they can be beneficial to each other by their integration as a holistic system along with their sub-stems of Software, Hardware, Machine Learning and Deep Learning.

Resilience of countries to COVID-19 correlated with trust

We characterized > 150 countries’ resilience to COVID-19 as the nationwide decay rate of daily cases or deaths from peak levels. Resilience to COVID-19 varies by a factor of ~ 40 between countries for cases/capita and ~ 25 for deaths/capita. Trust within society is positively correlated with country-level resilience to COVID-19, as is the adaptive increase in stringency of government interventions when epidemic waves occur. By contrast, countries where governments maintain greater background stringency tend to have lower trust within society and tend to be less resilient. All countries where > 40% agree “most people can be trusted” achieve a near complete reduction of new cases and deaths, but so do several less-trusting societies. As the pandemic progressed, resilience tended to decline, as adaptive increases in stringency also declined. These results add to evidence that trust can improve resilience to epidemics and other unexpected disruptions, of which COVID-19 is unlikely to be the last.

Strategies for Achieving Pre-emptive Resilience in Military Supply Chains

As technological advancement is rapidly evolving modern warfare, military supply chains are becoming more dynamic and complex with high vulnerability to unexpected disruptions. To increase their overall resilience against such unexpected disruptions, traditional approaches are no longer sufficient. To date, research on supply chain resilience has mainly focused on reactive responses and recovery strategies (post-disruption). Hence, the research gap addressed in this paper is that of identifying new and proactive strategies to enable pre-emptive resilience in military supply chains (pre-disruption). In this paper, the authors first provide a critical review of the pertinent literature and research conducted over the past 12 years. Following on from there, they identify new research directions for enabling pre-emptive resilience to aid military logistic planners in monitoring supply chains and strategic decision-making to maintain their resilience.

Crisis as Opportunity to Try Something New: Student-Centered Pedagogy During the Onset of COVID-19

The onset of the COVID-19 pandemic in the Spring of 2020 forced a sudden and unexpected disruption of the usual modes of schooling around the world. In the United States, lack of federal, state and district leadership left most teachers to negotiate the chaotic early months of the pandemic on their own. This study attempted to discover to what extent some US teachers used this crisis as an opportunity to jettison traditional teaching methods in favor of more engaging, student-centered practices, and examined whether teacher self-efficacy and facility with technology were related to that decision. Analysis of survey data from PK-12 teachers (n=178) found a near-universal reduction in use of student-centered teaching methods (SCMs) during the onset of COVID-19, especially among teachers who reported higher self-efficacy before the crisis (age and experience were insulating factors). On average, greater self-confidence before COVID-19 was associated with a greater <i>decrease</i> in the use of SCMs during the crisis. While TSE during the crisis was positively correlated with use of student-centered methods, the direction of the influence between those two variables could not be determined. In our analysis, the data seem to better support the theory that use of SCMs builds a sense of efficacy, rather than the traditional understanding that it is high TSE that empowers a teacher to use innovative pedagogy, but more study is needed to strengthen that theory. Technology versatility was correlated weakly with TSE in the COVID Onset Period, but we found no evidence of any correlation between technology versatility and SCM usage. The authors recommend further exploration through surveying a wider population and adding data sources beyond teacher self-reports.

COVID‐19 Lockdown Unravels the Complex Interplay between Environmental Conditions and Human Activity

During the COVID‐19 epidemic, draconian countermeasures forbidding nonessential human activities have been adopted in several countries worldwide, providing an unprecedented setup for testing and quantifying the current impact of humankind on climate and for driving potential sustainability policies in the postpandemic era from a perspective of complex systems. In this study, we consider heterogeneous sources of environmental and human activity observables, considered as components of a complex socioenvironmental system, and apply information theory, network science, and Bayesian inference to analyze their structural relations and nonlinear dynamics between January 2019 and August 2020 in northern Italy, i.e., before, during, and after the national lockdown. The topological structure of a complex system strongly impacts its collective behavior; therefore, mapping this structure is essential to fully understand the functions of the system as a whole and its fragility to unexpected disruptions or shocks. To this aim, we unravel the causal relationships between the 16 environmental conditions and human activity variables, mapping the backbone of the complex interplay between intervening physical observables—such as NO2 emissions, energy consumption, intervening climate variables, and different flavors of human mobility flows—to a causal network model. To identify a tipping point during the period of observation, denoting the presence of a regime shift between distinct network states (i.e., before and during the shock), we introduce a novel information‐theoretic method based on statistical divergence widely used in statistical physics. We find that despite a measurable decrease in NO2 concentration, due to an overall decrease in human activities, locking down a region as a climate change mitigation is an insufficient remedy to reduce emissions. Our results provide a functional characterization of socioenvironmental interdependent systems, and our analytical framework can be used, more generally, to characterize environmental changes and their interdependencies using statistical physics.

Designing a Resilient and Sustainable Logistics Network under Epidemic Disruptions and Demand Uncertainty

To face the new challenges caused by modern industry, logistics operations managers need to focus more on integrating sustainability goals, adapt to unexpected disruptions and find new strategies and models for logistics management. The COVID-19 pandemic has proven that unforeseen fragilities, negatively affecting the supply chain performance, can arise rapidly, and logistics systems may confront unprecedented vulnerabilities regarding network structure disruption and high demand fluctuations. The existing studies on a resilient logistics network design did not sufficiently consider sustainability aspects. In fact, they mainly addressed the independent planning of decision-making problems with economic objectives. To fill this research gap, this paper concentrates on the design of resilient and sustainable logistics networks under epidemic disruption and demand uncertainty. A two-stage stochastic mixed integer programming model is proposed to integrate key decisions of location–allocation, inventory and routing planning. Moreover, epidemic disruptions and demand uncertainty are incorporated through plausible scenarios using a Monte Carlo simulation. In addition, two resiliency strategies, namely, capacity augmentation and logistics collaboration, are included into the basic model in order to improve the resilience and the sustainability of a logistics chain network. Finally, numerical examples are presented to validate the proposed approach, evaluate the performance of the different design models and provide managerial insights. The obtained results show that the integration of two design strategies improves resilience and sustainability.

Visual Decision Support System Model for Managing Unemployment by Hybrid Education

Hybrid eeducation is an approach that helps in conducting face-to-face and online ddistance learning especially for formal education. However, during COVID-19 pandemic, formal education institutions including schools and universities as well as informal education tthrough the media for communities and industries are practiced by the lawmakers to manage workplace distancing. Food supply chain are experiencing unexpected disruptions due to interrelated activities that will determine supply chain performance and efficiency. This article explores the challenges faced by food industry and related sectors due to emergence impact of COVID-19 in Malaysia. Food supply chain management (FSCM) is a complex task involving interconnected activities and numerous stakeholders for a specific food product from raw materials to final product before it reaches the end customers. An overview of COVID-19 scenarios, followed by the impact of pandemic towards the activities in food supply chain, and the management issues that need to be addressed are presented. Several challenges identified are including the worldwide implication of the pandemic scenarios to food supply chain management and food supply chain activities, and indirect impact of supply chain disruption to labour force; unemployment and retrenchment. Finally, a visual decision support system (VDSS) model is developed to illustrate the main impacts of the pandemic scenario on food supply chain as well as addressing the labour management and opportunities. It has potential to provide general guidelines to educators, policy-makers, and decision-makers to deal with uncertainties after the pandemic.

Special Issue “Extreme Sciences and Engineering”

Due to uncertainties stemming from climate change, natural and man-made disasters, and extreme events, unexpected disruptions in the operation of our countries’ infrastructure may put the functioning of our societies and their economies at risk [...]