A new fuzzy approach based on BWM and fuzzy preference programming for hospital performance evaluation: A case study

Identifying and prioritizing resilient health system units to tackle the COVID-19 pandemic

Since human health greatly depends on a healthy and risk-free social environment, it is very important to have a concept to focus on improving epidemiology capacity and potential along with economic perspectives as a very influential factor in the future of societies. Through responsible behavior during an epidemic crisis, the health system units can be utilized as a suitable platform for sustainable development. This study employs the Best-Worst Method (BWM) in order to develop a system for identifying and ranking health system units with understanding the nature of the epidemic to help the World Health Organization (WHO) in recognizing the capabilities of resilient health system units. The purpose of this study is to identify and prioritize the resilient health system units for dealing with Coronavirus. The statistical population includes 215 health system units in the world and the opinions of twenty medical experts are also utilized as an informative sample to localize the conceptual model of the study and answer the research questionnaires. The resilient health system units of the world are identified and prioritized based on the statistics of "Total Cases", "Total Recovered", "Total Deaths", "Active Cases", "Serious", "Total Tests" and "Day of Infection". The present descriptive cross-sectional study is conducted on Worldometer data of COVID-19 during the period of 17 July 2020 at 8:33 GMT. According to the results, the factors of "Total Cases", "Total Deaths", "Serious", "Active Cases", "Total Recovered", "Total Tests" and "Day of Infection" are among the most effective ones, respectively, in order to have a successful and optimal performance during a crisis. The attention of health system units to the identified important factors can improve the performance of epidemiology system. The WHO should pay more attention to low-resilience health system units in terms of promoting the health culture in crisis management of common viruses. Considering the importance of providing health services as well as their significant effect on the efficiency of the world health system, especially in critical situations, resilience analysis with the possibility of comparison and ranking can be an important step to continuously improve the performance of health system units.

A Fuzzy Extension of Simplified Best-Worst Method (F-SBWM) and Its Applications to Decision-Making Problems

Today, most of the issues and challenges faced by managers and decision makers are complex and multifaceted. More clearly, due to the developments of technologies, emerging trends in various industries, competitive markets, and rapid and transformative changes in the business environment, managers and decision makers have faced an uncertain environments and issues that cannot be resolved definitively. The use of multi-criteria decision-making (MCDM) methods as a practical and decision-supporting tool allows managers to examine decision-making issues in various organizations and industries based on various criteria, alternatives, and objectives and make decisions with greater reliability. The use of fuzzy techniques and concepts in MCDM methods and their mathematical relationships makes it possible to consider complexities and uncertainties in decisions related to various issues and it can lead to better and more realistic decisions. In this paper, the simplified best-worst method (SBWM), which is one of the methods based on pairwise comparisons, has been developed using triangular fuzzy numbers (TFNs) to propose a fuzzy extension of SBWM (F-SBWM). Triangular fuzzy numbers in different symmetric and asymmetric forms have widely been used in MCDM approaches and pairwise comparisons. It is noteworthy that symmetric numbers are used when we are using equal division of the domain due to an increased ambiguity and lack of information. The proposed approach as a simplified fuzzy MCDM method helps managers and decision makers in various industries to solve decision-making problems under uncertainty without the need for complex calculations, specialized skills, and software packages. To check the feasibility and applicability of the proposed approach, two numerical examples and a computational experiment with real data are presented, and the results are analyzed and discussed. Furthermore, to check the robustness of the results obtained from the proposed approach, sensitivity analysis and comparison of methods have been performed.

RANKING RESIDENTIAL NEIGHBORHOODS BASED ON THEIR SUSTAINABILITY: A CM-BWM APPROACH

Population growth and rapid urbanization have consequences that are reflected in the economic, environmental, and social stability of city-residential neighborhoods. These impacts directly affect not only residents but also real estate markets and local governments. The professionals working in the latter entities have become increasingly concerned about urban sustainability and its strategic integration into their plans. Strategies have been implemented that focus on both addressing negative aspects of residential neighborhoods and enhancing positive features that can contribute to the continuous improvement of locals’ living conditions. This study applies the multiple-criteria decision analysis approach and a combination of cognitive mapping and the best-worst method (BWM) to identify the most relevant criteria and use these to rank residential neighborhoods according to their sustainability. To apply the selected techniques, two group meetings were held with a panel of decision makers. The results were validated by the panel members and the Funchal City Council councilor for urbanism, who concurred that the proposed ranking system facilitates the identification of the most sustainable residential neighborhoods. The contributions and limitations of the methodological approach are also discussed.

Healthcare performance management using integrated FUCOM-MARCOS approach: The case of India

AIMS

The goal of this research is to propose a simpler and more efficient model for evaluating healthcare establishments (HCEs). With this motivation, this study aims to discover key performance indicators (KPIs) that affect HCE performance, present a ranking model for KPIs in Indian HCEs, and evaluate Indian HCEs using the identified and prioritised KPIs.

MATERIAL AND METHODS

Through extensive literature review and expert opinions, this research identifies the various KPIs in HCEs, classifies them into six main categories, and prioritises them using the full consistency method (FUCOM). Further, well-known HCEs across northern India were evaluated and ranked using Measurement Alternatives and Ranking according to Compromise Solution.

RESULTS

The 'technology adoption related indicators' is found as the most important main KPIs, whereas 'adequate number of hospital beds and bathrooms (IE5)' as the most dominating sub-category KPIs. Also, amongst the 20 evaluated Indian HCEs 'healthcare establishment-1 (HCE1)' was found to be the best performing HCE while 'healthcare establishment-12 (HCE12)' was found to be the worst-performing HCE. The stability and consistency of the results are ascertained by performing sensitivity analysis and comparing the results with other existing methodologies.

CONCLUSION

The findings of this study are quite important for HCEs management to fully comprehend the key areas to improve upon so that managers can improve medical standards in a targeted manner. The developed prioritisation model and methodology shown in this paper will help and motivate managers and intellectuals of HCEs to evaluate and improve the HCE's performance.

A fuzzy hybrid decision-making framework for increasing the hospital disaster preparedness: The colombian case

The recent increase in the number of disasters over the world has once again brought to the agenda the question of preparedness of the hospitals, which are the most necessary units of healthcare pillar to resist these disasters. The COVID-19 epidemic disease, which has affected the whole world, has caused a large number of people to die in some countries simply because of the inadequate and incomplete planning and lack of readiness of hospitals. For this reason, determining the disaster preparedness level of hospitals is an important issue that needs to be studied and it is important in terms of disaster damage reduction. In this study, a fuzzy hybrid decision-making framework is proposed to assess hospital disaster preparedness. The framework covers three important decision-making methods. For the first phase, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) is used to assign relative weights for several disaster preparedness criteria considering uncertainty. Secondly, Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL) is applied to identify interrelations among these criteria and feedback. Finally, via the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, priorities of hospitals regarding disaster readiness are obtained. A case study involving the participation of 10 Colombian tertiary hospitals is carried out to show the applicability of this fuzzy hybrid approach.

An integrated approach for service quality evaluation of online health communities based on q-rung orthopair fuzzy linguistic aggregation operators

Online health communities (OHCs) have emerged as a significant platform for people communicating health information and self-healthcare management. In recent, the researches focusing on its performance measurement and the service quality evaluation have drawn intensive attention. Although some qualitative methods have made evaluation and analyses for the OHCs performance, the studies based on fuzzy multi-attribute decision making theory are rarely developed in the service quality evaluation of OHCs. In view of the complexity and uncertainty of evaluation mission, this paper develops an integrated evaluation approach of the OHC service quality based on the q-rung orthopair fuzzy linguistic aggregation operators. Firstly, we propose the cross-entropy of q-rung orthopair fuzzy numbers, which is applied in solving the optimal weight of indicators by a linear programming model. Next, the q-rung orthopair fuzzy linguistic power average (q-ROFLPA) and q-rung orthopair fuzzy linguistic partitioned dual Maclaurin symmetric mean (q-ROFLPDMSM) operators are developed for aggregating the assessment information and ranking the OHCs. Based on the proposed aggregation operators, the evaluation indicator system and an evaluation framework are constructed to accomplish the service quality evaluation of OHCs. Finally, a practical evaluation case of OHCs is provided to demonstrate the reliability and advantages of the proposed approach.

A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

Healthcare Industry 4.0 refers to intelligent operation processes in the medical industry. With the development of information technology, large-scale group decision making (GDM), which allows a larger number of decision makers (DMs) from different places or sectors to participate in decision making, has been rapidly developed and applied in Healthcare Industry 4.0 to help to make decisions efficiently and smartly. To make full use of GDM methods to promote the developments of the medical industry, it is necessary to review the existing relevant achievements. Therefore, this paper conducts an overview to generate a comprehensive understanding of GDM in Healthcare Industry 4.0 and to identify future development directions. Bibliometric analyses are conducted in order to learn the development trends from published papers. The implementations of GDM methods in Healthcare Industry 4.0 are reviewed in accordance with the paradigm of the general GDM process, which includes information representation, dimension reduction, consensus reaching, and result elicitation. We also provide current research challenges and future directions regarding medical GDM. It is hoped that our study will be helpful for researchers in the field of GDM in Healthcare Industry 4.0.

An efficiency-based interval type-2 fuzzy multi-criteria group decision making for makeshift hospital selection

Since makeshift hospitals have strong ability in blocking the spread of the virus, how to design some methods to select the reasonable sites of makeshift hospitals is vitally important for containing COVID-19. This paper investigates an efficiency-based multi-criteria group decision making (MCGDM) method by combining the best-worst method (BWM) and data envelopment analysis (DEA) in trapezoidal interval type-2 fuzzy (TrIT2F) environment. This MCGDM method is called TrIT2F-BWM-DEA, where the TrIT2F-BWM is used to determine the weights of criteria and decision-makers, and the TrIT2F-DEA is employed to rank alternatives by measuring their overall efficiencies. Based on cut set theory, the expectation and average expectation (AE) of TrIT2FSs are successively defined. To solve three key issues in the development of the TrIT2F-BWM, this paper proposes a flexible ranking relation of TrIT2FSs to transform the TrIT2F constraints, initiates an efficient theorem to normalize the TrIT2F weights, and designs an input-based consistency ratio to check the reliability of the determined weights. A fully TrIT2F-DEA model is originally built to measure the TrIT2F efficiencies of alternatives. The alternatives are finally ranked according to the AEs of alternatives’ TrIT2F efficiencies. A site selection case of Fangcang hospitals and some comparative analyses are provided to confirm the validity and merits of the proposed TrIT2F-BWM-DEA.

Performance Evaluation of Combining with Medical and Old-Age Care in Pension Institutions of China: A Two-Stage Data Envelopment Analysis

Purpose

The aim of this paper was to build a performance evaluation index system for the combination of medical and old-age care services in pension institutions of China.

Methods

A two-stage data envelopment analysis (DEA) is used to evaluate the performance of 30 pension institutions in China.

Results

The results show that the two-stage DEA accounted for a relatively high affiance of medical and nursing care services, but resource allocation still needs to be further optimized. Institutions with ineffective DEA need to reduce the five factors of operations, management, fixed assets, technology and services in the input dimension.

Conclusion

In the output dimension, the service evaluation effect and safety management effect need to be improved. The performance of combined old-age care and medical care in old-age institutions can be improved in terms of investment in fixed assets, methods of capital subsidies, supervision and management, as well as standardized operations.

An integrated fuzzy sustainable supplier evaluation and selection framework for green supply chains in reverse logistics

Green supply chain management considers the environmental effects of all activities related to the supply chain, from obtaining raw materials to the final delivery of finished goods. Selecting the right supplier is a critical decision in green supply chain management. We propose a fuzzy green supplier selection model for sustainable supply chains in reverse logistics. We define a novel hierarchical fuzzy best-worst method (HFBWM) to determine the importance weights of the green criteria and sub-criteria selected. The fuzzy extension of Shannon's entropy, a more complex evaluation method, is also used to determine the criteria weights, providing a reference comparison benchmark. Several hybrid models integrating both weighting techniques with fuzzy versions of complex proportional assessment (COPRAS), multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA), and the technique for order of preference by similarity to ideal solution (TOPSIS) are designed to rank the suppliers based on their ability to recycle in reverse logistics. We aggregate these methods' ranking results through a consensus ranking model and illustrate the capacity of relatively simple methods such as fuzzy COPRAS and fuzzy MOORA to provide robust rankings highly correlated with those delivered by more complex techniques such as fuzzy MULTIMOORA. We also find that the ranking results obtained by these hybrid models are more consistent when HFBWM determines the weights. A case study in the asphalt manufacturing industry is presented to demonstrate the proposed methods' applicability and efficacy.

Approaches for multicriteria decision-making based on the hesitant fuzzy best–worst method

Hesitant fuzzy preference relations (HFPRs) have been widely applied in multicriteria decision-making (MCDM) for their ability to efficiently express hesitant information. To address the situation where HFPRs are necessary, this paper develops several decision-making models integrating HFPRs with the best worst method (BWM). First, consistency measures from the perspectives of additive/multiplicative consistent hesitant fuzzy best worst preference relations (HFBWPRs) are introduced. Second, several decision-making models are developed in view of the proposed additive/multiplicatively consistent HFBWPRs. The main characteristic of the constructed models is that they consider all the values included in the HFBWPRs and consider the same and different compromise limit constraints. Third, an absolute programming model is developed to obtain the decision-makers’ objective weights utilizing the information of optimal priority weight vectors and provides the calculation of decision-makers’ comprehensive weights. Finally, a framework of the MCDM procedure based on hesitant fuzzy BWM is introduced, and an illustrative example in conjunction with comparative analysis is provided to demonstrate the feasibility and efficiency of the proposed models.

An Integrated Decision Support Model Based on BWM and Fuzzy-VIKOR Techniques for Contractor Selection in Construction Projects

Sustainable development of any country to some extent depends on successful accomplishment of construction projects, particularly infrastructures. Contractors have a key role in the success of these projects. Hence, the selection of a competent contractor as a complicated and hard decision process has a vital importance in the destiny of any construction project. Contractor selection is in essence a multicriteria decision-making that ought to encompass so many aspects of the project and the client’s requirements on one hand and the capabilities and past records of the contractors on the other hand. Failure in selecting a competent contractor may cause time and cost overruns; quality shortcomings; increasing in claims, disputes and change orders; and even failure of the project. In spite of deficiencies of selecting a contractor by the rule of “the lowest bid price”, it still prevails in many countries including Iran. In this paper, a new contractor selection model based on the best-worst method (BWM) and well-known Fuzzy-VIKOR techniques is proposed as a solution to overcome the deficiencies of the traditional “lowest bid price” rule. An illustrative example of a water channel construction project verified the applicability of the proposed model in practice.

Evaluating Life Cycle of Buildings Using an Integrated Approach Based on Quantitative-Qualitative and Simplified Best-Worst Methods (QQM-SBWM)

Evaluating the life cycle of buildings is a valuable tool for assessing sustainability and analyzing environmental consequences throughout the construction operations of buildings. In this study, in order to determine the importance of building life cycle evaluation indicators, a new combination method was used based on a quantitative-qualitative method (QQM) and a simplified best-worst method (SBWM). The SBWM method was used because it simplifies BWM calculations and does not require solving complex mathematical models. Reducing the time required to perform calculations and eliminating the need for complicated computer software are among the advantages of the proposed method. The QQM method has also been used due to its ability to evaluate quantitative and qualitative criteria simultaneously. The feasibility and applicability of the SBWM were examined using three numerical examples and a case study, and the results were evaluated. The results of the case study showed that the criteria of the estimated cost, comfort level, and basic floor area were, in order, the most important criteria among the others. The results of the numerical examples and the case study showed that the proposed method had a lower total deviation (TD) compared to the basic BWM. Sensitivity analysis results also confirmed that the proposed approach has a high degree of robustness for ranking and weighting criteria.

Identification of Relevant Criteria Set in the MCDA Process—Wind Farm Location Case Study

The paper undertakes the problem of proper structuring of multi-criteria decision support models. To achieve that, a methodological framework is proposed. The authors’ framework is the basis for the relevance analysis of individual criteria in any considered decision model. The formal foundations of the authors’ approach provide a reference set of Multi-Criteria Decision Analysis (MCDA) methods (TOPSIS, VIKOR, COMET) along with their similarity coefficients (Spearman correlation coefficients and WS coefficient). In the empirical research, a practical MCDA-based wind farm location problem was studied. Reference rankings of the decision variants were obtained, followed by a set of rankings in which particular criteria were excluded. This was the basis for testing the similarity of the obtained solutions sets, as well as for recommendations in terms of both indicating the high significance and the possible elimination of individual criteria in the original model. When carrying out the analyzes, both the positions in the final rankings, as well as the corresponding values of utility functions of the decision variants were studied. As a result of the detailed analysis of the obtained results, recommendations were presented in the field of reference criteria set for the considered decision problem, thus demonstrating the practical usefulness of the authors’ proposed approach. It should be pointed out that the presented study of criteria relevance is an important factor for objectification of the multi-criteria decision support processes.