Fuzzy Decision-making Approach Research Articles

Selection of optimal intensity measures for seismic performance evaluation of underground utility tunnel and internal pipeline system

Seismic intensity measures (IMs), which are closely related to both earthquake hazards and structural responses, play a critical role in the performance-based earthquake engineering (PBEE) framework. This paper aims to investigate and define the most representative IMs for probabilistic seismic demand model (PSDM) for the longitudinal utility tunnel and internal pipeline system. Similar to the requirements by the Chinese Code for seismic design of urban rail transit structures, the utility tunnel in this study is assumed being buried in four typical engineering site classes for the analyses. Soil-tunnel-pipeline interactions are simulated using a double-beam system resting on a nonlinear foundation. Soil-tunnel interaction, tunnel-pipeline interaction and joint connections are modeled as nonlinear springs with different mechanical properties. A series of 17 pairs of outcrop ground-motion records and 27 commonly used IMs are selected in the analyses. Engineering demand parameter (EDP) is defined based on the maximum joint opening and the maximum strain of the utility tunnel and internal pipeline, respectively. The selected IMs for predicting the seismic responses of tunnel-pipeline systems are tested using different criteria, i.e., correction, efficiency, practicality, and proficiency. The final decision-making procedure employs a fuzzy multi-criteria decision method. The numerical results indicate that: (1) The velocity-related IMs are the most representative IMs based on the four criteria for structures buried in four sites of different conditions; (2) The optimal seismic IMs vary with different criteria, site classes and structure types. According to the fuzzy multi-criteria decision approach, optimal IMs for the tunnel-pipeline system are PGV, PGV, EPV and HI for site classes I – IV, respectively. The proposed optimal IMs can be utilized to construct seismic fragility curves of utility tunnel, internal pipeline and tunnel-pipeline systems in four different site classes.

Safety of agricultural machinery and tractor maintenance planning with fuzzy logic and MCDM for agricultural productivity

Productivity is one of the most important measures used to determine the growth and development level of countries or sectors. A wide variety of projects have been planned and implemented to increase agricultural productivity. The productivity to be obtained in agriculture; Soil conditions, climate, seeds, fertilizer, pesticides, labor and agricultural mechanization directly affect it. Agricultural mechanization is the realization of agricultural activities by using energy together with agricultural tools and machines. Agricultural mechanization; It is an important agricultural production technology that helps increase agricultural productivity. Due to the inadequate maintenance planning of agricultural machinery, agricultural machinery cannot be utilized at the desired level in agricultural production. Most agricultural equipment is subject to frequent changes in speed and direction of movement while operating. Damage that can be seen on a single machine; It also causes other machines to malfunction. During the year, especially in the months when agricultural activity is high, excessive working tempo can cause tractors to malfunction. The breakdown of tractors causes disruptions in agricultural activities. In addition, the breakdown of tractors increases the repair costs. Since there is no tractor maintenance planning, farmers face interruptions in agricultural activities due to tractor malfunction. However, tractor malfunctions may cause cost and economic losses. For these reasons, there is a need for appropriate maintenance planning of agricultural machinery in order to continue agricultural activities without disruption. Maintenance planning; It consists of a set of preventive activities to improve the reliability and availability of any system. The main purpose of this study is to determine and rank the importance level weights of the criteria that are important for agricultural machinery maintenance planning using the fuzzy AHP method. Fuzzy AHP method, which provides ease of application, was preferred in determining the Criterion Weights. The research proposes a framework to determine the weights of appropriate criteria for care planning selection through a combined approach of fuzzy multi-criteria decision making involving relevant stakeholders. On the basis of the prioritization of criteria of tractor maintenance planning (TMP), it was found from the ranking that checking for all fluid levels (TMP1) ranked first. This respectively is followed by checking for general conditions (TMP4), checking for tires and wheels (TMP2) and checking for batteries (TMP3). With the results of the study, a guide was created for farmers and other stakeholders, as well as decision makers, to help plan the maintenance of machines in better working conditions. It is also thought that this study will be encouraging for other studies.

A combined fuzzy decision-making approach to evaluate the MBT plant location alternatives

A combined fuzzy decision-making approach to evaluate the MBT plant location alternatives

An integrated picture fuzzy operational competitiveness ratings group decision approach for evaluating the enterprise digital transformation capability

An integrated picture fuzzy operational competitiveness ratings group decision approach for evaluating the enterprise digital transformation capability

Retraction Note: Adoption of the sustainable circular supply chain under disruptions risk in manufacturing industry using an integrated fuzzy decision-making approach

Retraction Note: Adoption of the sustainable circular supply chain under disruptions risk in manufacturing industry using an integrated fuzzy decision-making approach

Stochastic energy management of a microgrid incorporating two-point estimation method, mobile storage, and fuzzy multi-objective enhanced grey wolf optimizer

In this study, the stochastic energy management, and scheduling of a renewable microgrid involving energy sources and dynamic storage is performed considering energy resource and demand uncertainties and demand response (DR) using the two-point estimation method (2 m + 1 PEM). The three-dimensional objective function is defined as maximizing the renewable hosting capacity and minimizing the operation cost, and emission cost minimization. The decision variables include installation location and size of the renewable resources and mobile energy storage system (MESS), determined using a multi-objective enhanced grey wolf optimizer (MOEGWO) improved based on the logistic chaotic mapping integrated with fuzzy decision-making approach. The simulations are implemented for several cases of employing MESS, DR, and uncertainties to investigate the proposed approach's efficacy. The MOEGWO performance is confirmed to solve the ZDT and CEC'09 functions according to some well-known algorithms. Then, the performance of the MOEGWO is evaluated on the stochastic energy management and scheduling of the renewable microgrid. The results indicate that considering the dynamic MESS causes reducing the operation and emission costs by 23.34% and 34.78%, respectively, and increasing the renewable hosting capacity by 7.62% in contrast to using the static MESS. Also, the stochastic problem-solving considering uncertainties showed that operation and emission costs are raised, the renewable hosting capacity is decreased, and the uncertainty impact is reduced in the condition of DR application. So, the results validated the proposed methodology's effectiveness for minimizing the operation and emission costs and maximizing the renewable hosting capacity. Moreover, the superior capability of the MOEGWO is confirmed in comparison with the multi-objective particle swarm optimization to obtain lower operation and emission costs and higher renewable hosting capacity.

A hybrid neuro fuzzy decision-making approach to the participants of derivatives market for fintech investors in emerging economies

The risk profiles of investors play an important role in the success of derivative products investments. It is also necessary to determine the appropriate investor profile according to the conditions of a country. This protects investors from major losses. Therefore, there is a strong need for a new study that analyzes the risk profiles of derivative products in emerging economies. This study aims to identify the most appropriate investor risk profile for derivative instruments in emerging economies. It constructs a novel decision-making model. In this model, the facial action coding system, quantum theory, spherical fuzzy sets, and multi-stepwise weight assessment ratio analysis (M-SWARA) methodology are integrated to minimize uncertainty in this process. First, three main participants of derivatives for financial technology investors are evaluated using the quantum spherical fuzzy M-SWARA technique. After that, seven emerging economies are ranked based on this performance using quantum spherical fuzzy technique for order of preference by similarity to ideal solution. The main contribution is that an appropriate investor profile is identified for financial derivative investments in emerging economies. One of the important originalities of this study is the development of a new technique called M-SWARA by making some improvements to the classical stepwise weight assessment ratio analysis method. It is concluded that hedging is the most appropriate purpose for making investments in derivative products in emerging economies. It is also identified that China has the greatest performance among the seven emerging economies regarding the appropriate purpose for making derivative investments. For the effective management of this problem in these countries, it is recommended to prevent the speculative use of derivative products with legal regulations.

A Stratified Fuzzy Decision-Making Approach for Sustainable Circular Supplier Selection

Circular economy has attracted great attention as it meets environmental and social requirements. Selecting appropriate suppliers considering circular, and sustainability aspects for all industries is of great significance. However, addressing sustainable circular supplier selection (SCSS) problems is a difficult task due to the uncertainty in real-life cases in terms of possible future events and experts’ judgments. This article aims to address the SCSS problem considering the impacts of possible future events via a multiple-criteria decision-making framework. For this purpose, stratification theory is utilized to develop and practice a novel framework called stratified fuzzy full consistency method to identify the optimal weight coefficients of criteria considering the possible impacts of future events. Later, a novel extension of mixed aggregation by comprehensive normalization technique under fuzzy environment is developed to prioritize suppliers with respect to sustainability and circular economy criteria. To prove the practicability of the developed methodology, outcomes of a real case study related to a textile company in Turkey are presented. Furthermore, sensitivity analyses and a comparative analysis are conducted to illustrate the proposed model's performance under different conditions. According to the results, information disclosure, interests and rights of employees, and child labor are identified as top three criteria with values of 0.1112, 0.1102, and 0.1110, respectively.

Prioritizing the indicators of energy performance management: a novel fuzzy decision-making approach for G7 service industries

Ensuring energy performance management is important in many ways, such improvement of energy efficiency and decrease of energy costs are reduced. There are various indicators of the effectiveness of energy performance management of buildings. Due to this situation, businesses need to make the necessary improvements for the development of these factors. Nonetheless, these actions cause an increase in the costs of the companies. Hence, among these actions, the more important ones need to be identified. Owing to this issue, businesses can use their limited budgets for more priority indicators. The purpose of this study is to evaluate the main indicators of energy performance management systems. In this way, a new model is proposed to make a priority analysis for the hospitals. Firstly, five indicators of energy performance management systems are selected by considering ISO 50006 standards. Furthermore, these indicators are weighted by using Spherical fuzzy CRITIC. Secondly, G7 countries are examined with fuzzy RATGOS technique. Identification of the most significant indicators of the energy performance systems is an important novelty of this study. The most significant methodological novelty of this study is proposing a new technique to the literature named RATGOS. It is understood that energy efficiency is the most crucial indicator of energy performance management. Furthermore, it is also identified that France is the most successful G7 economy with respect to the energy performance management. Japan and United States have also high performance in this respect. It is recommended that necessary actions should be taken to increase energy efficiency. By conducting an energy audit, energy consumption data is analyzed so that energy losses and inefficiencies can be detected. This assessment provides opportunities for energy efficiency and helps identify improvement strategies.

Fuzzy decision-making framework for explainable golden multi-machine learning models for real-time adversarial attack detection in Vehicular Ad-hoc Networks

This paper addresses various issues in the literature concerning adversarial attack detection in Vehicular Ad-hoc Networks (VANETs). These issues include the failure to consider both normal and adversarial attack perspectives simultaneously in Machine Learning (ML) model development, the lack of diversity preprocessing techniques for VANETs communication datasets, the inadequate selection guidelines for real-time adversarial attack detection models, and the limited emphasis on explainability in adversarial attack detection. In this study, we propose an original fuzzy decision-making framework that incorporates multiple fusion standpoints. Our framework aims to evaluate multi-ML models for real-time adversarial attack detection in VANETs, focusing on three stages. The first stage involves identifying and preprocessing Dedicated Short-Range Communication (DSRC) data using standard and fusion preprocessing approaches. Two communication scenarios, normal and jammed, are considered, resulting in two DSRC datasets. In the second stage, we develop multi-ML models based on the DSRC datasets using standard preprocessing and feature fusion preprocessing for dataset-1 and dataset-2, respectively. The third stage evaluates the multi-ML models using a fuzzy decision-making approach based on the Fuzzy Decision by Opinion Score Method (FDOSM) and an adversarial attack decision fusion matrix. The External Fusion Decision (EFD) settings of the FDOSM address individual ranking variance, provide a unique rank and select the best model. Experimental results demonstrate that the K-Nearest Neighbors Algorithm (kNN) model achieves the highest explain score of 0.2048 in dataset-1 using standard preprocessing, while the Random Forest (RF) model applied to dataset-2 using fusion preprocessing emerges as the most robust and golden model against adversarial attacks, with a score of 0.1819. This finding suggests that the fusion preprocessing approach using Principal Component Analysis (PCA) is more suitable for addressing normal and adversarial attack perspectives. Furthermore, our fuzzy framework undergoes evaluation in terms of systematic rank, sensitivity analysis, explainability analysis, and comparison analysis. Overall, this framework provides valuable insights for researchers and practitioners in VANETs, informing the execution, selection, and interpretation of multi-ML models to tackle adversarial attack detection problems effectively. The new fuzzy framework demonstrates that multi-ML models based on feature fusion preprocessing are more effective.

Development of a new index for assessing the inherent safety level of chemical processes using a multi-criteria fuzzy decision-making approach

Estimating and evaluating the inherent safety of processes pose a significant challenge in the field of inherent safety. Indicators are commonly employed tools to address this challenge. However, one of the weaknesses of risk-based indicators is their assumption of equal weight and importance for all indicators, despite the fact that the impact of each indicator may vary. This study aimed to investigate whether assigning weights to indicators can enhance the performance of inherent safety indices. To achieve this objective, all indicators that influence inherent safety were identified through systematic literature review and expert interviews. Subsequently, the final weights of these indicators were determined using multicriteria decision-making methods such as BWM, DEMATEL, and fuzzy ANP. By integrating these indicators, a Weighted Inherent Safety Index (WISI) was developed, accompanied by two subsidiary inherent safety indices: process inherent safety (consisting of 5 indicators) and chemical material inherent safety (consisting of 9 indicators). The WISI index serves as a quantitative measure to assess various synthesis routes of a chemical substance and determine the safer option during the conceptual design phase. In a case study, the production process of methyl methacrylate involving 6 different synthesis routes was selected, and the WISI index was applied to validate its effectiveness. Overall, this approach introduces a novel perspective to enhance the performance of inherent safety indices by assigning appropriate weights to indicators, thereby promoting a more comprehensive evaluation of process safety.

An integrated linguistic Pythagorean fuzzy decision-making approach for risk analysis of offshore wind turbine

The evaluation of risk factors within the context of condition-based maintenance (CBM) for offshore wind turbines has been a challenging issue. In this paper, a novel risk analysis method that combines the fuzzy weighted with zero inconsistency (FWZIC) technique and the DEMATEL method under the linguistic Pythagorean fuzzy environment is proposed. Firstly, 75 risk factors linked to offshore wind turbines are identified by leveraging analysis and prior literature, which are modeled using linguistic Pythagorean fuzzy sets (LPFSs) to facilitate the complexity of expert opinions in the face of uncertainty. Then, the hybrid expert weight calculation method and the FWZIC-based criteria weight calculation method are introduced to determine the weights of the experts and the criteria while considering uncertainty. Next, the extended DEMATEL method under the linguistic Pythagorean fuzzy environment is adopted to determine the significance and influence of the pinpointed risk factors, and an illustrative cause–effect diagram is crafted to dissect the interconnections between these factors. Through this intricate analysis, the cause factors and effect factors specific to offshore wind turbines are identified, and the results obtained from the proposed method are validated through sensitivity analysis and comparative analysis. The results of this study could provide valuable recommendations and references for the implementation of CBM in offshore wind turbines.

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators

A clinically important loss in a person’s understanding, emotive power, or conduct is a symptom of a mental disorder. It generally occurs for genetic, psychological, and/or cognitive reasons and is accompanied by discomfort or limitationin significant functional areas. It can be handled using techniques similar to those used to treat chronic conditions (i.e., precautions, examination, medication, and recovery). Mental diseases take a variety of forms. Mental disorder is also identified as mental illness. The latter is a more usual phrase that incorporates psychological problems, psychosocial disorders, and (other) states of mind linked to considerable discomfort, operational limitations, or danger of loss of sanity. To rank the most prevalent types of mental disorders is a multi-attribute decision-making issue and thus this article aims to analyze the artificial intelligence-based evaluation of mental disorders and rank the most prevalent types of mental disorders. For this purpose, here we invent certain aggregation operators under the environment of the bipolar complex fuzzy set such as bipolar complex fuzzy Schweizer-Sklar prioritized weighted averaging, bipolar complex fuzzy Schweizer-Sklar prioritized ordered weighted averaging, bipolar complex fuzzy Schweizer-Sklar prioritized weighted geometric, bipolar complex fuzzy Schweizer-Sklar prioritized ordered weighted geometric operators. After that, we devise a procedure of decision-making for bipolar complex fuzzy information by employing the introduced operators and then take artificial data in the model of bipolar complex fuzzy set to rank the most prevalent types of mental disorders. Additionally, this article contains a comparative study of the introduced work with a few current works for exhibiting the priority and superiority of the introduced work.

Evaluating the risks of the internet of things in renewable energy systems using a hybrid fuzzy decision approach

Using the Internet of Things (IoT) and its integration into Renewable Energy Systems (RES) leads to energy planning, management of energy consumption and demand, and, as a result, sustainably matches energy supply and demand. There are several risks in implementing and integrating IoT in RES, which is an obstacle to successfully implementing these systems. Therefore, this research aims to assess the risks of implementing IoT in RES based on the hybrid Multi-Criteria Decision-Making (MCDM) approach under uncertain conditions in Iran. In this study, to specify the importance of criteria, the Fuzzy Stepwise Weight Assessment Ratio Analysis (F-SWARA) method is utilized, and to assess the risks of IoT integration in RES, the usage of the Fuzzy Combined Compromise Solution (F-CoCoSo) method is suggested. The following examines the sensitivity analysis and validation with fuzzy WASPAS, fuzzy ARAS, and fuzzy TOPSIS techniques. The findings showed that the proposed approach had adequate robustness and flexibility for risk assessment, and risks of investment, infrastructure development, and privacy and security were introduced as the most critical risks.

An Intuitionistic Fuzzy Decision Support for Agile Project Selection Problem

In the early 1990s, organizations started to use information technology and software engineering, which encouraged the companies for employing project management methodologies to survive and then advance in competitive technologic environment. Use of project management methodologies provides efficient planning, budgeting and scheduling processes and high management quality for companies. A lot of different methodologies were developed and used to reach better ways of defining the project requirements, analyzing the problem, and implementing it in a systematic manner. In 2001, agile project management methodology came forward in response to cope with waterfall project methodology’s limitations that arise from unpredictability of technology evolution, customer requirements, and unstable business environments. In this study, intuitionistic fuzzy COPRAS method, which aims to obtain a solution relative to the ideal solution, is used to rank agile project alternatives and identify the best performing one among them. Intuitionistic fuzzy sets are used to deal with loss of information and hesitation in data that may occur in operations with fuzzy numbers. The application of the proposed intuitionistic fuzzy decision-making approach is illustrated by conducting a case study.

A novel multiple criteria ranking approach for determining the Most Valuable Player (MVP) of a sport season: A numerical study from NBA league

Team sports have gained a significant place in society, generating debates about team strengths, player merits, and ultimately, determining champions. The National Basketball Association (NBA) offers various individual awards, with the MVP award holding paramount importance. Selecting the MVP necessitates a comprehensive approach considering both statistical performance and team success throughout the season. This study presents a fuzzy decision-making approach to compare the performance of players and identify the MVP of the League. A case study for the 2022-2023 NBA season is presented in this study, wherein 535 players' regular season statistics are analyzed. Correlation analysis is employed to eliminate the criteria which are dependent on each other. Therefore, the number of criteria has been decreased from twenty to seven which are defined as key metrics: (i) matches won, (ii) points scored per game, (iii) shooting percentage, (iv) rebounds per game, (v) assists per game, (vi) steals per game, and (vii) blocks per game. After correlation analysis, Full Consistency Method (FUCOM) is employed to determine the importance weights of the criteria. We have employed a specific normalization procedure and employed information axiomatic design (IAD) to rank players based on their total information contents. The case study proves the feasibility and applicability of the proposed methodology for multiple criteria ranking problem. Future research may focus on position-specific rankings, providing more accurate assessments, and extending analysis to youth leagues for draft day decisions.

Optimizing sustainable industry investment selection: A golden cut-enhanced quantum spherical fuzzy decision-making approach

This study aims to rank sustainable industry alternatives in emerging markets based on the directional impact relations of the environmental, social, and governance (ESG) index components for a socially and environmentally conscious investment strategy. To achieve this goal, we employ a golden cut-enhanced quantum spherical fuzzy decision-making approach. Specifically, we first use a quantum spherical fuzzy DEMATEL technique to identify the impact-relation directions and the weights of the ESG criteria set. Second, we employ the extended TOPSIS with the quantum spherical fuzzy sets to rank the industry alternatives concerning their directional ESG performances. The findings show that (i) H20 Emissions, Innovation, Community Investment, Gender Equity, Human Rights, and CSR Strategy are the main influencing factors based on their impact-relations directional scores, (ii) Resource Usage, Product Responsibility, and Shareholders’ Rights are the set of criteria under the influence of remaining ESG, (iii) Innovation is the strongest ESG performance criterion, whereas Human Rights is the weakest, (iv) technology and communication are the best-performing industries based on the directional ESG index performance scores, whereas real estate and basic materials industries are the worst performing. The study provides valuable and actionable insights for companies that aim to make socially responsible investments.

Ratio-Type Estimator for Estimating the Neutrosophic Population Mean in Simple Random Sampling under Intuitionistic Fuzzy Cost Function

Survey sampling has a wide range of applications in biomedical, meteorological, stock exchange, marketing, and agricultural research based on data collected through sample surveys or experimentation. The collected set of information may have a fuzzy nature, be indeterminate, and be summarized by a fuzzy number rather than a crisp value. The neutrosophic statistics, a generalization of fuzzy statistics and classical statistics, deals with the data that have some degree of indeterminacy, imprecision, and fuzziness. In this article, we introduce a fuzzy decision-making approach for deciding a sample size under a fuzzy measurement cost modeled by an intuitionistic fuzzy cost function. Our research introduces neutrosophic ratio-type estimators for estimating the population mean of the neutrosophic study variable YN∈[YL,YU] utilizing all the indeterminate values of the neutrosophic auxiliary variable XN∈[XL,XU] rather than only the extreme values XL and XU. Three simulation studies are carried out to explain the proposed methods of parameter estimation, sample size determination, and efficiency comparison. The results reveal that the proposed neutrosophic class of estimators produces more accurate and precise estimates of the neutrosophic population mean than the existing neutrosophic estimators in simple random sampling, which is the ultimate goal of inferential statistics.

A facial expression and expert recommendation fuzzy decision-making approach for sustainable business investments within the metaverse world

Commercial transactions in the metaverse have become more popular in recent years. Sustainable investments are a prerequisite to metaverse survival and growth. There are various factors that have an impact on the performance of sustainable business investments, such as financial analysis and organizational effectiveness. However, each of these improvements leads to increased investment and costs. These new investments may create a heavy financial burden on businesses. In this context, there is a great need to conduct a priority analysis of the criteria affecting these investments in the metaverse. This study uses a balanced scorecard to analyze the alternatives of sustainable business investments in the metaverse. For this purpose, a hybrid quantum spherical fuzzy decision-making approach is proposed by using the facial action coding system and collaborative filtering to measure the weights of the balanced scorecard perspectives with the extended decision-making trial and evaluation laboratory (DEMATEL) and to rank the alternatives of sustainable business investments within the metaverse with the extended technique for order preference by similarity to ideal solution (TOPSIS). The analysis perspectives are finance, customers, internal process, learning, and growth. The alternatives are determined as real estate, commerce, tourism, education, and community development for sustainable virtual investment decisions in the metaverse. The novelty of this study is to construct a new decision-making approach based on facial expressions, expert recommendation systems, and the quantum spherical fuzzy sets, as well as a new concept of sustainable business investments within the metaverse. It has been determined that the organizational effectiveness of the enterprises is very important in realizing sustainability goals and increasing sustainability performance. Similarly, tourism is the most suitable sustainable virtual investment area in the Metaverse universe. Using resources to ensure organizational effectiveness helps businesses reduce energy, water, and raw material consumption. This situation contributes to achieving sustainability targets as it allows the consumption of fewer natural resources.

A novel framework for investigating organizational adoption of AI-integrated CRM systems in the healthcare sector; using a hybrid fuzzy decision-making approach

CRMS (Customer Relationship Management system) is a brand-new technology that assists hospitals in managing their relationship with patients. Organizations’ analysis methods of huge customer-related data have been subjected to a massive change because of the Artificial Intelligence (AI) integrated CRMS. This integration enables healthcare institutes to quickly and precisely analyze the tremendous volume of patient-related data in order to make accurate, and automated decisions. On the contrary to prior surveys in the healthcare domain, which mainly focused on the individual acceptance of innovation, this study tries to consider both organizational and human-related factors to provide a better understanding of the CRMS adoption in hospitals. This paper will be the first of its kind to analyze and categorize parameters contributing to CRMS adoption in healthcare by introducing an integrated framework, including Human, Technology, Organization, Environment, and Cost (HTOEC) dimensions. The fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is applied to find the interdependence among adoption dimensions and variables. The Maximum Mean de-Entropy (MMDE) algorithm is exploited to choose the optimum threshold value in the DEMATEL method. The fuzzy Analytic Network Process (ANP) is deployed to prioritize adoption parameters. Based on the results, Environment and Human dimensions are the most crucial determinants of CRMS adoption in Iranian hospitals.