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The facility in an emergency system could be immobilized because of the huge destructive power of an irregular emergency and the uncertainty of the time, place and scale of occurrence. So facility failure scenarios must be considered at the time of location. The purpose of this paper is to establish a location model based on the worst facility failure, the objective of which is to minimize the cost and cover the demand maximally. It is demonstrated that location choice, considering facility failure, has significant meaning when considering economic benefit and covering the demand.

A bi‐level programming model which studies the facility location is established by using the methods of scenario analysis and robust optimization. It is compared with a classic location model, without considering facility failure, from the points of view of economic benefit and maximal covering demand.

Compared to the classic location model, without considering facility failure, it is demonstrated that the location model which considers facility failure can save more costs from the economic benefit point of view and, from the maximal covering of the demand point of view, has a higher covering ratio. So facility failure scenario should be considered in the location of an emergency facility.

The paper studies facility location based on the worst scenario, from the two aspects of economic benefits and maximal covering demand.

Facility location decisions are critical and should be taken after strategic evaluations. Globalization and integration of economies make such decisions further complex and risk prone. The purpose of this paper is to identify and assess the risk factors to be considered while taking new facility location decision associated with global supply chain and device the methodology. A grey-based multi-criteria decision-making (MCDM) approach is used for this purpose, which also takes in to account the uncertainty in decision making. Such approach enables final decision to be more real and practical. The paper also highlighted and discussed the criteria on the basis of which the management can select the best suitable site.

The risk factors related to facility location for a global firm are identified. To select the location of a global facility with least risk, grey-based MCDM approach is formulated. This grey-based MCDM is demonstrated using the hypothetical case of an industrial valve manufacturing global firm. The grey approach is used to analyse location alternatives based on various decision criteria for extracting comparative ranking.

The paper presents a tool for strategic and planning level. It helps supply chain managers to identify the risks related to a candidate location. Then it guides the supply chain manager at strategic level to find the least risky location for a manufacturing facility.

This paper demonstrates the grey-based MCDM approach for determining less risky location to locate a new manufacturing unit so that practitioners can use this approach for taking other strategic decisions. The supply chain configuration can be decided subsequently which will yield more practical results and the decision taken will be more fruitful for firm.

The extensive literature review reveals that there are many models in the literature that addressed the issue of risk minimization in supply chain, but it was also noticed that there are limited number of models that minimize risk in locating a global facility considering the uncertainty of data in decision making. This is the first time that grey-based MCDM approach is formulated and used to find most suitable facility location under risk.

As a traditional area, engineering management has always been at the core of the research study in academia. In the current uncertain world, more and more attention is paid to risk management. New risks in many practical engineering management problems require new computational methods and tools. The purpose of this paper is to introduce the state‐of‐the‐art research in engineering management and risk management in China.

The collected papers are briefly reviewed and positioned in existing work.

The special issue includes a variety of perspectives on engineering management and risk management from the Great China area.

The paper shows that all the papers address interesting research problems.

Power outages which may be triggered, for example, by natural hazards and system failures are a common phenomenon, associated with large impacts on society including the healthcare sector. Minimising adverse impacts effectively requires an analysis of possible impacts and the identification of measures aiming at reducing vulnerability and increasing resilience.

To systematically identify impacts as well as preparation, mitigation and recovery (PMR) measures, a moderated workshop with participants representing different healthcare sub‐sectors in Germany was conducted and complemented by semi‐structured interviews and a thorough analysis of literature. Impacts were determined for three scenarios of power outage duration, <8, 8‐24 and >24 hours.

Whereas hospitals are in general well prepared with respect to shorter outages, due to obligatory emergency power in Germany, outpatient medical care, nursing homes (NH) and, in particular, home‐care nursing are early affected. Failure of these sub‐sectors puts additional strains on hospitals. If outages last more than one day and are associated with failure of other critical infrastructures (CIs), especially water supply, hospitals may be severely affected. Effective preparation and mitigation measures identified based on a facility‐specific impact analysis, as well as good cooperation between actors, may reduce impacts.

The largely case‐study‐based literature is complemented by a systematic and extensive analysis of direct and indirect impacts on the main healthcare sub‐sectors in Germany, followed by an identification of specific PMR measures. As a novelty outage duration is explicitly accounted for. Also, interdependencies between the healthcare sub‐sectors as well as dependencies on other CI are considered.

This paper aims to focus on identifying key health-care issues amenable to digital twin (DT) approach. It starts with a description of the concept and enabling technologies of a DT and then discusses potential applications of DT solutions in healthcare facilities management (FM) using four different scenarios. The scenario planning focused on monitoring and controlling the heating, ventilation, and air-conditioning system in real-time; monitoring indoor air quality (IAQ) to monitor the performance of medical equipment; monitoring and tracking pulsed light for SARS-Cov-2; and monitoring the performance of medical equipment affected by radio frequency interference (RFI).

The importance of a healthcare facility, its systems and equipment necessitates an effective FM practice. However, the FM practices adopted have several areas for improvement, including the lack of effective real-time updates on performance status, asset tracking, bi-directional coordination of changes in the physical facilities and the computational resources that support and monitor them. Consequently, there is a need for more intelligent and holistic FM systems. We propose a DT which possesses the key features, such as real-time updates and bi-directional coordination, which can address the shortcomings in healthcare FM. DT represents a virtual model of a physical component and replicates the physical data and behavior in all instances. The replication is attained using sensors to obtain data from the physical component and replicating the physical component's behavior through data analysis and simulation. This paper focused on identifying key healthcare issues amenable to DT approach. It starts with a description of the concept and enabling technologies of a DT and then discusses potential applications of DT solutions in healthcare FM using four different scenarios.

The scenarios were validated by industry experts and concluded that the scenarios offer significant potential benefits for the deployment of DT in healthcare FM such as monitoring facilities’ performance in real-time and improving visualization by integrating the 3D model.

In addition to inadequate literature addressing healthcare FM, the study was also limited to one of the healthcare facilities of a large public university, and the scope of the study was limited to IAQ including pressure, relative humidity, carbon dioxide and temperature. Additionally, the study showed the potential benefits of DT application in healthcare FM using various scenarios that DT experts validated.

The study shows the practical implication using the various validated scenarios and identified enabling technologies. The combination and implementation of those mentioned above would create a system that can effectively help manage facilities and improve facilities' performances.

The only identifiable social solution is that the proposed system in this study can manually be overridden to prevent absolute autonomous control of the smart system in cases when needed.

To the best of the authors’ knowledge, this is the only study that has addressed healthcare FM using the DT approach. This research is an excerpt from an ongoing dissertation.

Limited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues.

A Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion.

Results of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value.

This paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.

Supply chain network design is an important strategic decision that firms make considering both the short‐ and long‐term consequences of the network's performance. The typical design approach implicitly assumes that, once designed, the facilities and the links will always operate as planned. In reality, however, facilities and the links connecting them, fail from time to time due to poor weather, natural or man‐made disasters, or a combination of any other factors. This work aims to propose a design framework that addresses the facility and link failures explicitly by accounting for their impact on a network's performance measures of efficiency and robustness.

The study incorporated a robustness metric for evaluating the resiliency of supply chains in the case of a network disruption. This robustness metric is based on expected losses incurred due to network failures. It defines efficiency and robustness in terms of operational cost and expected disruption cost (EDC), respectively. The EDC is defined in terms of loss of opportunity cost incurred due to not meeting demand on time after a disruption has occurred. The study used a scenario planning approach and formulated a mixed integer linear program model with the objective of maximizing both efficiency and robustness. It also evaluates the trade‐offs between efficiency and robustness.

The resulting supply chain is much more reliable in the long term since we have shown that a significant amount of robustness can be built into the system without compromising a lot on efficiency.

This work demonstrates a methodology which incorporates such disaster scenarios into the design of a supply chain network. This leads to a more reliable supply chain which would lead to higher profitability and lower disruption rates.

The purpose of the paper is to bring attention to documentary credits and the efforts to reduce debt obligations in credit history is recognized as an important source of uncommitted bank earnings. Credit risk has a significant impact on the stability of the banking system. This paper identifies the types of credit risk in the banking supply chain.

The authors model the types of credit risk using the intuitive fuzzy failure modes and effects analysis (IFMEA) and intuitive fuzzy cognitive mapping. The population of the study that is needed for the interviews and expert panels comprises senior managers and experts of a leading bank in Iran. The respondents are experienced in credit and banking risk and were selected through judgment sampling and snowballing.

The findings suggest that reducing the risks of the foreign letters of credit contracts can mitigate the risk in the agricultural sector, the specific risks of rent-to-own contracts, the risk of the long-term facilities and the specific risk of the domestic letter of credit contracts.

This research investigates Iran Tejart Bank’s credit risk, formulates a model of the types of credit risk present and analyzes them using the intuitive fuzzy failure modes and effects analysis and intuitive fuzzy cognitive map. Through this credit risk model, one can then facilitate risk management for better financial stability. Also, the model can be used to evaluate the risk indicators.

To achieve the optimum performance of electric transmission power system performance, the possibility of generators’ failure and the consequences are amongst the most important and real assumptions which should be taken into consideration. This paper aims to recognize the most influential factors on generators’ failures that can have a deep effect on the total cost and environmental issues. The integrated proposed approach is useful for investigating the generators’ failure effects on the performance of electric power transmission grids from the economic and environmental perspectives. In other words, the cost and pollution minimization policies are considered to decrease the unfavorable generators’ failure effects on electric power flow.

The data used in this study are gathered from a real case in USA in first step, the influential generator points that their failure has a significant effect on the objective function, have been recognized. Then, different failure scenarios are defined, and the optimum values in each of these scenarios through the GAMS modeling software are found. Consequently, by using a two-level factorial design approach, the critical generators across the power grid are determined.

The results show that by using such information, it is possible to detect the significant nodes in the power system grid and have a better maintenance plan. In addition, by means of this analysis and changing the capacity of main generators, it is possible to significantly reduce the operation costs. By comparing the indexes in case of the generator’s location, it seems that some of them are critical because of their capacity and position in the network (as their failure causes infeasibility in the model). Also, some of these deficiencies caused considerable index changes and critical consequences.

The integrated proposed approach is useful for investigating the generators’ failure effects on the performance of electric power transmission grids from the economic and environmental perspectives. In other words, the cost and pollution minimization policies are considered to decrease the unfavorable generators’ failure effects on electric power flow.

This paper endeavors to recognize the most influential factors on generators’ failures that can have a deep effect on the total cost and environmental issues.

The integrated proposed approach is useful for investigating the generators’ failure effects on the performance of electric power transmission grids from the economic and environmental perspectives. In other words, the cost and pollution minimization policies are considered to decrease the unfavorable generators’ failure effects on electric power flow.