Prioritizing the barriers of manufacturing during COVID-19 using fuzzy AHP

COVID-19 has wreaked havoc on the global economy, supply chains, and government, posing an unparalleled health threat. The manufacturing sector was one of the most disruptive systems in the world at the time of the COVID-19 pandemic. Most manufacturing companies have faced a lock-down situation and are focusing on the production of essential products. Furthermore, COVID-19 has altered customer behavior. The short-term and long-term effects of COVID-19 on the manufacturing sector must be evaluated to hasten recovery and build preparedness measures should another such disruption occur. The limitations affecting the construction system during this period were discussed and prioritized in this study. The ambiguous nature of human thinking makes it difficult to evaluate the qualitative parameters;hence, it is preferred to incorporate an approach that converts the variables into triangular fuzzy numbers to better represent the values of the criteria. A fuzzy analytical hierarchical procedure (FAHP) is applied to evaluate the limitation criteria in an ambiguous environment. "Growing demand for existing products" is considered the heaviest limit after "financial stagnation" and "setback in logistics services." The study results will help the manufacturing company in formulating and implementing strategies to overcome the pandemic situation. © 2023 River Publishers. All rights reserved.

Identifying Critical Factors Affecting the Resilience of Additive Manufacturing Architecture Supply Chain

Building a resilient and stable supply chain has become an important strategy for many countries. Studies have shown that the application of additive manufacturing (AM) technology in construction can help offset the negative impact of "black swan events” on supply chains. This study examines the construction industry based on AM technology and analyzes the impact of changes in the industry chain on the supply chains. The specific factors that affect the resilience of AM construction supply chains were identified through literature research and expert interviews, including 7 dimensions and 21 secondary indicators. An intuitionistic fuzzy analytic hierarchy process (IFAHP) evaluation model was established. Finally, an example of an AM construction manufacturer, YC Enterprise, was introduced to quantify the various factors and determine the weights. The results show that the essence of building a supply chain with AM is creating a closed-loop supply chain. The impact of AM construction manufacturers on supply chain resilience (SCR) is the most critical, followed by that of regulatory authorities and general contractors. The AM construction SCR assessment index system and evaluation method constructed in this paper have important significance in filling the gap in the quantitative evaluation of the impact of AM on supply chains.

Student financial support and sustainable recovery based on fuzzy-analytic hierarchy process comprehensive method

In the post-COVID-19 era, many university students face financial distress due to macroeconomic downturns. Helping college students overcome financial difficulties is an important aspect of sustainable recovery. Based on the theory of student satisfaction and the perspective of new financial support systems, this study constructs a performance evaluation system for financial support for financially distressed students in colleges and universities. To comprehensively evaluate financially distressed students for financial support from the perspective of satisfaction, an empirical study is conducted by taking the authors' college as an example according to the fuzzy Analytic Hierarchy Process analysis and evaluation model. The results show that, according to the characteristics of students, the students' families' characteristics should be considered and carefully sorted, and on this basis, the appropriate selection criteria for financially distressed students are selected. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Fuzzy AHP Approach for Supply Chain Strategy Selection: A Post-Pandemic Scenario

Supply chains have been severely disrupted globally due to the COVID-19 pandemic. The paper examined the strategic responses of automobile firms for meeting supply chain challenges they face post-pandemic. Data were collected using a specifically designed structured questionnaire from supply chain experts working with leading automobile manufacturing firms in India. The fuzzy analytic hierarchy process (FAHP), as a part of a multi-criteria decision-making model using R programming, was applied to identify and rank the choice of supply strategies using various criteria, such as lead time, logistics cost (holding cost, carrying cost, warehousing cost, handling cost), and the need of products. Two-wheeler and four-wheeler manufacturing firms were selected for the study. Logistics cost was found to be a dominant criterion, followed by a demand for products and lead time, which helped select an appropriate supply chain strategy. Buffering was observed to be the best strategic choice, and automation and robotics applications were the least preferred ones both for two-wheelers and four-wheeler manufacturing companies. The findings would be helpful to both practitioners and researchers in evaluating diverse strategic choices, especially under the risk and disruptions faced by business firms in the supply chain. © 2023, Associated Management Consultants Pvt. Ltd.. All rights reserved.

Collaborative intuitionistic fuzzy-AHP to evaluate simulation-based analytics for freight transport

Globally, the transportation and logistics sector is facing economic disruptions owing to geopolitical tensions and post-COVID-19 global economic downturns. This disruption places more pressure on transportation companies to review their work methods and processes. Coupling data and model-driven approaches is essential for developing effective and efficient resilience strategies. To address this issue, this study provides an overview of the appearance of simulation in business analytics. However, a thorough review of the literature based on the PRISMA search process allowed us to identify that none of the previous studies could highlight the role or evaluate the hybridization between business analytics and simulation and their joint use in freight transportation. Moreover, this study proposes a collaborative framework based on the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique to select a business analytics-enabled simulation architecture. This study contributes to the freight transport sector by setting up an updated list of criteria and sub-criteria necessary for business analytics evaluation and enriches the literature by applying the IF-AHP technique to a concrete case of implementing data analytics and simulation. This study also suggests future directions to enrich the academic literature and offers insights to improve the framework for other use cases. © 2023 Elsevier Ltd

A Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak.

The use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was ER facilities (14.4%), while Procedures and protocols evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network.

Fuzzy AHP approach to evaluate incentive factors of high-tech talent agglomeration

To compete with the speedy revolution of high technological innovation and restarted economy for the post-COVID-19 period in China, governments and organizations should be active in attracting high-tech talent to enhance independent and indigenous R&D capability. Talent agglomeration effectiveness is the strongest endogenous force pushing competitiveness for regional economy and industrial development. Due to the complexity of high-tech talent agglomeration, there are still considerable gaps to evaluate the incentive factors. This study evaluates the influential indicator system by using a hybrid fuzzy set theory extended Analytic Hierarchy Process (AHP) approach for proximity to reality from individual, organizational and environmental dimensions. The statistical analysis is adopted to verify the results of fuzzy AHP analysis. This research explores the founding that individual incentives are more important than environmental factors, and environmental incentives are more influential than organizational incentives. Job satisfaction, welfare system, and geographical location are the highest ranking factors. High-tech start-ups should give priority to combine geographical location with political support to reserve site selection or firm relocation for a great effectiveness of high-tech talent agglomeration. © 2022 Elsevier Ltd

Analyzing the impact of COVID-19 vaccination requirements on travelers' selection of hotels using a fuzzy multi-criteria decision-making approach.

In the later stages of the COVID-19 pandemic, hotels are taking various measures to balance pandemic prevention and business operations. Some hotels require travelers to be fully vaccinated prior to check-in, while others do not. In the latter type of hotels, fully vaccinated travelers may encounter others who are not vaccinated. All of these have created constraints for travelers to choose suitable hotel accommodation during this time. To address this issue, a fuzzy multi-criteria decision-making approach is proposed in this study to help traveler choose suitable hotel accommodation. In the proposed methodology, firstly, hotels are divided into two types considering their requirements for COVID-19 vaccination. Travelers are then asked to list the key factors to consider when choosing between these two types of hotels. To derive the priorities of these key factors, the proportionally calibrated fuzzy geometric mean (pcFGM) method is proposed. Subsequently, the fuzzy VIsekriterijumskoKOmpromisnoRangiranje (fuzzy VIKOR) method is applied to evaluate and compare the overall performances of different types of hotels for recommendations to travelers. The applicability of the proposed methodology is illustrated by a real case study. According to the experimental results, most hotels did not request travelers to be full vaccinated. Nevertheless, the hotels recommended to travelers covered both hotel types.

Assessment of COVID-19 impact on sustainable development goals indicators in Egypt using fuzzy analytic hierarchy process.

COVID-19 significantly influences the Sustainable Development Goals (SDGs) in both developed and developing countries. Within the 2030 agenda, Egypt is likely to face enormous negative ramifications from the virus spread. As a result, efficient control of the adverse repercussions of this virus is critical to achieving this objective. This research assesses indicators of specific SDGs in reflecting COVID-19 impact by conducting several questionnaire surveys among experts in Egypt. The scope of this research is limited to addressing poverty alleviation (SDG1), hunger abatement (SDG2), healthcare promotion (SDG3), sustainable economic growth (SDG8), and climate change mitigation (SDG13). The indicators are prioritized using the relative importance index, weighted aggregated sum product assessment technique for order preference by similarity to an ideal solution, and fuzzy analytic hierarchy process. The rankings are finally aggregated using an approach based on the half-quadratic theory. The results reveal that the most significant indicators in reflecting the COVID-19 impact are the share of population living below the international poverty line, undernourishment prevalence, official health sector support, annual gross domestic product per capita growth rate, and number of disaster deaths for SDG1, SDG2, SDG3, SDG8, and SDG13, respectively. Recognizing and ranking the indicators could help decision-makers understand the behavior of SDG indicators in light of COVID-19. The research findings could assist policymakers in making informed decisions to reduce the pandemic effects and sustain achieving SDGs by 2030.

Digital Information Mobility Schema: A Data-Flow Model Featuring Risk-Resilient Approach Towards Effective Construction Worksite Synergy Utilizing Fuzzy-Analytic Hierarchy Process

The increasingly effective managing of risks in construction projects requires the stakeholders to collaborate, resulting in the need to integrate the use of Building Information Modelling (BIM) to mitigate the risks in project collaboration. Our understanding of strategic planning of BIM adoption amidst a pandemic is still limited, and it is widely accepted that COVID-19 is a long-term pandemic that require a constant and innovative range of mitigation approaches to protect public health. The significant construction advances emphasize remote work and digital tools that assist in the project's on-time completion. A fully digitalized approach is necessary for service continuity and rapid processing, particularly during a pandemic. Therefore, this study develops an adaptive digital collaboration framework based on Cloud-Based BIM technology to reduce risks while increasing workplace productivity and mobility. It resulted in a new way of managing the project information, enhancing the design team collaboration, and transforming 2D plans into 3D models. It integrates information to take a building through a virtual construction process long before it is completed, and each team member has access to the most up-to-date and current project information. © 2022 The authors and IOS Press.

Evaluating the Preparedness of Indian States against COVID-19 Pandemic Risk: A Fuzzy Multi-criteria Decision-Making Approach.

The preparedness of Indian states and union territories (UTs) against the COVID-19 pandemic has been evaluated. Ten parameters related to demographic, socioeconomic, and healthcare aspects have been considered and the performances of 27 states and three UTs have been evaluated applying the Fuzzy Analytic Hierarchy Process. Opinions of medical experts have been considered to ascertain the relative importance of decision criteria as well as subcriteria. The scores of various states and UTs in each of the decision subcriteria have been calculated by using the secondary data collected from authentic sources. It is found that Kerala and Bihar are the best prepared and worst prepared states, respectively, to combat COVID-19 pandemic. Karnataka, Goa, and Tamil Nadu have very good preparedness whereas Chhattisgarh, Jharkhand, and Bihar have very poor preparedness. Maharashtra, the most affected state in India, has average preparedness. As around 650 million people are vulnerable due to the poor and very poor preparedness of their states, the country needs to make region specific mitigation strategies to combat the COVID-19 pandemic and the preparedness map will be helpful in that direction.

Fuzzy MCDM Approach for Building Private Cloud Medical Records

This paper aims to introduce the application of Fuzzy MCDM approach to tackle the selection issue in building private cloud medical records. Vast amount medical records currently require a solution to build cloud data storage. Cloud based data storage is currently required to deal with continuous demand of electronic medical records in particular during covid 19. Private cloud data center is proposed to overcome the issue of storing daily medical records from various sources. The study proposes a Fuzzy MCDM model based on the Analytic Hierarchy Process (AHP) in choosing the best open source cloud data storage for establishing private cloud medical records. There are many perspectives to be compromised during the selection process of several open source solutions and all pairwise comparison should meet maximum inconsistecy rate of 0.1. It is finally concluded that OwnCloud is the most suitable open source solution for solving the given problem. © 2022 IEEE.

COVID-19 Risk Assessment of Occupations Using Interval Type 2 Fuzzy Z-AHP & TOPsIs Methodology

The pandemic spread of COVID-19 caused by a virus that affects the respiratory system represents a dramatic threat to life. People have been practicing social distancing by working from home in recent months since it is an excellent solution to reduce one’s exposure to COVID-19. However, many occupations do not have this chance due to the necessity to attend the workplace. Besides, some professions require close contact with infected people such as medicine and nursing, while others such as logging and gardening have a low level of risk in this respect. However, while occupations such as medicine and nursing can take precautions against the virus at a very good level, the knowledge of occupations such as logging and gardening is weak against it. Prioritizing occupations based on these conflicting criteria is an important task under the vagueness and impreciseness of human evaluations. In this paper, a novel fuzzy AHP & TOPSIS methodology is proposed, where the reliability and restriction of the expert assessments are considered by interval-valued type 2 fuzzy numbers. A real case study including five experts, 14 occupations, and seven criteria is presented. A comparative analysis is also given to validate the proposed methodology. © 2022 Old City Publishing, Inc.

Sustainable Assessment for Biomass Power Plant Location during the COVID-19 Pandemic

Renewable energy plays an important role in energy sustainability and environmental friendliness, for example in terms of biomass power, and is essential for generating electric power, which has a significant impact on sustainable energy for the future. The objective of this research, therefore, is to propose a sustainable method of assessment for biomass power plant site selection for electric power supply for field hospitals during the COVID-19 pandemic in Thailand In this research, based upon overlay map layers as an empirical study, the pandemic, as well as environmental friendliness, are considered in Thailand The Fuzzy Analytic Hierarchy Process (FAHP) has been applied in order to determine the weighting of the criteria. Furthermore, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) has also been applied in order to rank alternative locations. The contribution of this research lies in the development of an approach that is flexible in terms of sustainable assessment, in terms of both subjective and objective evaluation measures, in order to reduce bias in the decision-making process, as well as to include natural disaster assessment in providing guidelines for the location selection of a biomass power plant from the perspective of environment friendliness.

A Fuzzy Bayesian Network Based on Fault Tree for Vaccine Safety Risks Analysis

Against the Covid-19 background, vaccine safety has aroused the wild attention of all social areas. However, the factors that cause vaccine safety risks are complicated and meanwhile, data is difficult to obtain, making it a challenge for analyzing vaccine safety risks quantitatively. This paper concretises the issue of vaccine system safety by creatively proposing an analytical framework for the problem of uncertainty. First, the paper focuses on the whole process of vaccine safety, analyses risk factors affecting vaccine safety in development, approval, production, transportation, and supervision of vaccines in order to build a vaccine risk assessment system. The proposed framework is then used to construct a Bayesian network early warning system for vaccine risk. To address the difficulty of obtaining data, the probability of safety risks occurring throughout the process is calculated by combining expert knowledge and fuzzy set theory to obtain uncertainty data. In response to structural complexity, a comprehensive framework is constructed using fault trees and Bayesian networks to capture the correlation between risk factors. This analytical framework can provide guidance to governments and vaccine-related companies in their decision-making to prevent vaccine safety issues. Finally, sensitivity analysis revealed a high probability of vaccine risk in the transport process. © 2021 IEEE.

A fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach for suitable hotel recommendation amid the COVID-19 pandemic.

Cities around the world have reopened from the lockdown caused by the COVID-19 pandemic, and more and more people are planning regional travel. Therefore, it is a practical problem to recommend suitable hotels to travelers amid the COVID-19 pandemic. However, it is also a challenging task since the critical factors that affect hotel selection amid the COVID-19 pandemic may be different from those usually considered. From this perspective, the fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach is proposed in this study for hotel recommendation. The proposed methodology not only considers the critical factors affecting hotel selection amid the COVID-19 pandemic, but also establishes a systematic mechanism, that is, enhanced fuzzy geometric mean, to simultaneously improve the accuracy and efficiency of the recommendation process. The fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach has been successfully applied to recommend suitable hotels to 10 travelers for regional trips amid the COVID-19 pandemic.

A calibrated piecewise-linear FGM approach for travel destination recommendation during the COVID-19 pandemic.

After months of lockdown due to the COVID-19 pandemic, more people are planning regional trips because overseas travel is still not feasible. However, choosing a suitable travel destination during the COVID-19 pandemic is challenging because the factors critical to the selection process are very different from those usually considered. Furthermore, without sufficient literature or data for reference, existing methods based on psychological analyses or mining past experiences may not be applicable. Consequently, a fuzzy multi-criteria decision-making method - the calibrated piecewise-linear fuzzy geometric mean (FGM) approach - is proposed in this study for travel destination recommendation during the COVID-19 pandemic. The contribution of this research is twofold. First, the critical factors that affect the selection of a suitable travel destination during the COVID-19 pandemic are discussed. Second, the accuracy and efficiency using existing fuzzy analytic hierarchy process (FAHP) methods have been enhanced. The calibrated piecewise-linear FGM approach has been successfully applied to recommend suitable travel destinations to fifteen travelers for regional trips in Taiwan during the COVID-19 pandemic.