A novel interval-valued fuzzy soft decision-making method based on CoCoSo and CRITIC for intelligent healthcare management evaluation

Assessment of sustainable waste management alternatives using the extensions of the base criterion method and combined compromise solution based on the fuzzy Z-numbers

A number of factors have contributed to the increase in waste production and diversity of waste, such as the increase in population, alterations in consumption patterns, economic development, income changes, urbanization, and industrialization. The production of different types of waste, such as electronic, urban, hospital, and industrial waste, makes it necessary to classify waste accurately and recognize effective criteria for waste management. To design and operate waste management systems, it is necessary to understand the sources and types of waste, as well as information about their composition and rate of production. As a result, this study aims to rank 21 types of waste according to Iran's economic, social, and environmental criteria, as well as 13 sub-criteria related to those criteria. For this aim, proposed a novel decision-making approach based on the extension of the base criterion method (BCM) and combined compromise solution (CoCoSo) methods under fuzzy Z-numbers. Additionally, sensitivity analysis and comprehensive analysis are conducted on the results of the criteria and alternatives of sustainable waste management. Based on the results of this study, direct profit and reduced landfill are the most important criteria for assessing sustainable waste management alternatives. According to the results of this study, the sub-alternative of industrial metal waste is the most important waste management option. Examining the next sub-alternative ranks under sustainable waste management options (mobile, communication equipment, and battery) shows that electronic waste requires more attention for recycling and sustainable waste management.

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.

Breast cancer treatment planning using a novel spherical fuzzy CRITIC-REGIME

Breast cancer is the leading cause of cancer-related deaths, and choosing a suitable treatment plan for this disease has proved difficult for oncologists owing to the variety of criteria and alternatives that must be considered during the decision-making process. Since prospective treatment options influence patients’ health-related quality of life in a variety of ways, a methodology that can completely and objectively evaluate alternative treatments has become an essential issue. This paper proposes a novel multi-criteria decision-making (MCDM) methodology by integrating the CRiteria Importance Through Intercriteria Correlation (CRITIC) and the REGIME techniques and handles the problem of breast cancer treatment selection problem. CRITIC enables the determination of objective criterion weights based on the decision matrix, while REGIME ranks the options without the need for lengthy computations or normalization procedures. The suggested methodology is demonstrated in a spherical fuzzy atmosphere, which allows decision experts to independently express their degrees of membership, non-membership, and hesitancy in a broad three-dimensional spherical space. In the numerical example provided, three oncologists evaluate four breast cancer treatment alternatives, namely, surgery, radiotherapy, chemotherapy, and hormone therapy, with respect to five criteria, which are disease or tumor type, stage of disease, patient type, side effects, and financial status of the patient. The tumor type is determined to be the most important assessment criterion, and surgery is selected as the best course of action. The stability and validity of the proposed methodology are verified through sensitivity and comparative studies. The discussions, limitations, and future research avenues are also given within the study.

Application of Integrated BWM Fuzzy-MARCOS Approach for Coating Material Selection in Tooling Industries

The life of metal forming dies and the efficiency of tooling industries depends on the mechanical and wear properties of tool steel. These properties can be further improved by depositing the ceramic coating on heat-treated tool steel. Numerous coating materials with various excellent features are already available commercially; however, the selection of the best coating material is still an immense challenge for users. Compared to various studies related to material selection problems in the contrasting area of utilization, remarkably, little research work has been done in tooling industries. In the present work, we have identified eight coating materials (alternatives) and nine evaluation criteria under the consultation of an expert in the tooling application and tribological field. To deal with this coating material selection problem, an integrated fuzzy-multi attributed decision-making method is proposed which comprises best worst method and fuzzy-Measurement Alternatives and Ranking according to the Compromise Solution method. This integrated fuzzy- multi attributed decision-making method is used to evaluate the alternatives, and the obtained results were scrutinized via utilizing various sensitivity analysis procedures. In the first phase of analysis, seven scenarios of criteria weight change were used, which was derived by the best-worst method; dynamic matrices are used in the second phase of analysis. In the third and fourth phases, obtained ranks were compared with those obtained by different weight calculation methods and ranking methods, respectively. In the present study, AlCrN/TiAlN coating (alternative Cm₅) was found to be the best coating material based on the results obtained after sensitivity analysis. Further, in this study, we have proposed a novel method that helps to solve the coating material selection problem or any kind of selection complications.

Multicriteria group decision making for COVID-19 testing facility based on picture cubic fuzzy aggregation information

The information aggregation of cubic fuzzy numbers and picture fuzzy numbers have played an important role in decision making. This paper introduces a novel approach to address the problem of testing facility of COVID-19 under picture cubic fuzzy environment. As the picture cubic fuzzy set is a generalized fuzzy structure to handle more uncertainty and ambiguity in decision making problems We discuss its various properties. Based on geometric aggregation operators and Hamacher operations, we introduce some Hamacher geometric aggregation operators under picture cubic fuzzy information. Namely, picture cubic fuzzy Hamacher weighted geometric aggregation operator, picture cubic fuzzy Hamacher hybrid geometric operator, picture cubic fuzzy Hamacher order weighted geometric aggregation operator. Discuss some properties of the defined operators. To verify the importance of the proposed operators, develop multicriteria group decision making (MCGDM) algorithm under picture cubic fuzzy environment and apply this strategy for the selection of an authentic laboratory for COVID-19 test. Further to validate the supremacy of our proposed operators, we present a comparative analysis with pre-existing aggregation operators. Results show that the proposed technique is more effective and suitable for MCGDM problems.

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology.

Evaluation and dynamic prediction of ecological security from the perspective of sustainable development: a case study of Shaanxi Province, China

How to measure the overall level of regional social economy, resources, and environment and how to grasp the coordinated development between them has become a hot issue. In this paper, the driving force-pressure-state-response (DPSR) model is used to build an ecological security index (ESI) system to measure the overall ecological security level of social economy, resources, and environment. In addition, dynamic nonlinear auto regressive (NAR) neural network is used to predict the ESI level to achieve the purpose of early warning. First, the results show that the weight of the employment rate, the proportion of students in colleges, and the per capita consumption level are relatively high, which play an important role for the ecological security level of Shaanxi Province. Second, Xi'an City has been the best level in ecological security level, the ecological security of Southwest Shaanxi is relatively good, which is related to its economic development and comparative advantage geographical conditions, while the ecological security level of Weinan and Shangluo are poor. Third, the ESI of most cities in Shaanxi Province is maintained at grade III. The ESI in Shaanxi has an upward trend from 2000 to 2006; however, the trend of this increase has not been maintained, and nearly half of the cities in Shaanxi have slightly decreased the ecological security level. Four, the ESI of Xi'an and Hanzhong will remain at a high level in the future, while the ecological security situation of Shangluo, Weinan, and Yulin probably become very poor in the next years.

Extended EDAS method for multiple attribute decision making in mixture z-number environment based on CRITIC method

In multiple attribute decision making (MADM) issues, the ambiguity, imprecision, and imperfection of assessment information may lead to inadequate decision-making results. However, the Z-number suggested by Zadeh in 2011 could somehow prevent this problem. For MADM issues with unknown attributes weights, an extended Distance from the Average Solution (EDAS) method is proposed under a mixture Z-number environment. In addition, the Criteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights of the criterion, which is easy to calculate and avoids subjective forecasts. A novel illustrative example is provided to demonstrate the feasibility, validity, and practicability of the presented method, and is compared with existing decision methods. The outcome indicates that the suggested method can solve complicated decision-making problems.

Toward the Nash Equilibrium Solutions for Large-Scale Pentagonal Fuzzy Continuous Static Games

This study aims to characterize and clarify a pentagonal fuzzy continuous static game (PF-CSG) that constraints and cost functions are fuzzy numbers. Pentagonal fuzzy numbers characterize their fuzzy parameters. The $\alpha -$ Pareto optimal solution concept has specified. The decomposition approach has applied to decompose the problem into subproblems each of them having smaller and independent subproblems. In addition, the Nash equilibrium solution concept was used to obtain the solutions of these subproblems. The advantages of this study are the players independently without collaboration with any of the others and that each player seeks to minimize the cost function. Also, the information available to each player consists of the cost function and constraints. An illustrated numerical example has discussed for proper understanding and interpretation of the proposed concept.

Adapting Urban Transport Planning to the COVID-19 Pandemic: An Integrated Fermatean Fuzzy Model

The critical worldwide problem of adapting urban transport planning to COVID-19 is for the first time comprehensively addressed and solved in this study. It primarily aims to help transport planners increase the resilience of transport systems. Firstly, a multi-level decision-making hierarchy structure based on four main criteria and 17 sub-criteria is introduced for relevant stakeholders to provide a practical framework for assessing existing transport plans. Then, a three-stage integrated Fermatean fuzzy model for adapting urban transport planning to the pandemic is presented. The model hybridizes the method based on the removal effects of criteria (MEREC) and combined compromise solution (CoCoSo) method into a unique methodological framework under the Fermatean fuzzy environment. A case study provides decision-making guidelines on how to adapt transport plans to COVID-19 in the real-world context of Belgrade, Serbia. The research findings show that the pandemic significantly changed the priorities of transport planning strategies and measures. "Non-motorized travel" is now the best alternative since its numerous short-term measures lead to better transport service. The major advantages of the introduced model are higher flexibility and a more precise fusion of experts' preference information. The integrated Fermatean fuzzy model could be used for adapting other emerging problems to COVID-19.

Deformation Analysis of an Ultra-High Arch Dam under Different Water Level Conditions Based on Optimized Dynamic Panel Clustering

During the operation period, the deformation of an ultra-high arch dam is affected by the large fluctuation of the reservoir water level. Under the dual coupling of the ultra-high dam and the complex water level conditions, the traditional variational analysis method cannot be sufficiently applied to its deformation analysis. The deformation analysis of the ultra-high arch dam, however, is very important in order to judge the dam safety state. To analyze the deformation law of different parts of an ultra-high arch dam, the panel data clustering theory is used to construct a Spatio-temporal characteristic model of dam deformation. In order to solve the difficult problem of the fluctuating displacement of dam deformation with water level effect, three displacement component indexes (absolute quantity, growing, and fluctuation) are proposed to characterize dam deformation. To further optimize the panel clustering deformation model, the objective weight coefficient of clustering comprehensive distance is calculated based on the CRITIC (CRiteria Importance Through Inter-criteria Correlation) method. The zoning rules of the ultra-high arch dam are established by using the idea of the CSP (Constraint Satisfaction Problem) index, and the complex water level of the reservoir is simulated in the whole process. Finally, the dynamic cluster analysis of dam deformation is realized. Through a case study, three typical working conditions including the rapid rise and fall of water level and the normal operation are calculated, and the deformation laws of different deformation zones are analyzed. The results show that the model can reasonably describe the deformation law of an ultra-high arch dam under different water levels, conveniently and intuitively select representative measuring points and key monitoring parts, effectively reducing the analysis workload of lots of measuring points, and improve the reliability of arch dam deformation analysis.

Evaluating the sustainability of smart technology applications in healthcare after the COVID-19 pandemic: A hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence

During the COVID-19 pandemic, some smart technology applications were more effective than had been expected, whereas some others did not achieve satisfactory performance. Consequently, whether smart technology applications in healthcare are sustainable is a question that warrants investigation. To address this question, a hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence was proposed in this study and then used to evaluate the sustainability of smart technology applications in healthcare. The contribution of this research is its subjective evaluation of the sustainability of smart technology applications followed by correction of the evaluation outcome on the basis of the applications' objective performance during the COVID-19 pandemic. To this end, a fuzzy nonlinear programming model was formulated and optimised. In addition, the impact of several major global events that occurred during the pandemic on the sustainability of smart technology applications was considered. The proposed methodology was applied to evaluate the sustainability levels of eight smart technology applications in healthcare. According to the experimental results, three applications-namely healthcare apps, smartwatches, and remote temperature scanners-are expected to be highly sustainable in healthcare, whereas one application, namely smart clothing, is not.

Taxonomy-based multiple attribute group decision making method with probabilistic uncertain linguistic information and its application in supplier selection

The optimal supplier selection in medical instrument industries could be considered a classical MAGDM issue. The probabilistic uncertain linguistic term sets (PULTSs) could depict uncertain information well and the Taxonomy method is appropriate to compare various alternatives according to their merits and utility degree from studied attributes. In such paper, we develop a Taxonomy method for probabilistic uncertain linguistic MAGDM (PUL-MAGDM) with the completely unknown attribute weights. Above all, the score function’s definition is utilized to derive the weights of attribute based upon the CRITIC method. In addition, the probabilistic uncertain linguistic development pattern (PULDP) is improved and the smallest development attribute value from the positive ideal solution under PULTSs is calculated to determine the optimal alternative. In the end, taking the supplier selection in medical instrument industries as an example, we demonstrate the usage of the developed algorithms. Based on this, the comparison of methods is conducted with existing methods, such as PUL-TOPSIS method, the PULWA operator, the PUL-EDAS method and the ULWA operator. The results verify that the decision-making framework is valid and effective for supplier selection. Thus, the advantage of this designed method is that it is simple to understand and easy to compute. The designed method can also contribute to the selection of suitable alternative successfully in other selection issues.

Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC)

The weights of criteria in multi-criteria decision-making (MCDM) problems are essential elements that can significantly affect the results. Accordingly, researchers developed and presented several methods to determine criteria weights. Weighting methods could be objective, subjective, and integrated. This study introduces a new method, called MEREC (MEthod based on the Removal Effects of Criteria), to determine criteria’ objective weights. This method uses a novel idea for weighting criteria. After systematically introducing the method, we present some computational analyses to confirm the efficiency of the MEREC. Firstly, an illustrative example demonstrates the procedure of the MEREC for calculation of the weights of criteria. Secondly, a comparative analysis is presented through an example for validation of the introduced method’s results. Additionally, we perform a simulation-based analysis to verify the reliability of MEREC and the stability of its results. The data of the MCDM problems generated for making this analysis follow a prevalent symmetric distribution (normal distribution). We compare the results of the MEREC with some other objective weighting methods in this analysis, and the analysis of means (ANOM) for variances shows the stability of its results. The conducted analyses demonstrate that the MEREC is efficient to determine objective weights of criteria.

A Hesitant Fuzzy Combined Compromise Solution Framework-Based on Discrimination Measure for Ranking Sustainable Third-Party Reverse Logistic Providers

Customers’ pressure, social responsibility, and government regulations have motivated the enterprises to consider the reverse logistics (RL) in their operations. Recently, companies frequently outsource their RL practices to third-party reverse logistics providers (3PRLPs) to concentrate on their primary concern and diminish costs. However, to select the suitable 3PRLP candidate requires a multi-criteria decision making (MCDM) process involving uncertainty owing to the presence of many associated aspects. In order to choose the most appropriate sustainable 3PRLP (S3PRLP), we introduce a hybrid approach based on the classical Combined Compromise Solution (CoCoSo) method and propose a discrimination measure within the context of hesitant fuzzy sets (HFSs). This approach offers a new process based on the discrimination measure for evaluating the criteria weights. The efficiency and practicability of the present approach are numerically demonstrated by solving an illustrative case study of S3PRLPs selection under a hesitant fuzzy environment. Moreover, sensitivity and comparative studies are presented to highlight the robustness and strength of the introduced methodology. The result of this work concludes that the introduced methodology can recommend a more feasible performance when facing with determinate and inconsistent knowledge and qualitative data.