Vise Kriterijumska Optimizacija I Kompromisno Resenje Research Articles

Integrated entropy- VIKOR approach for ranking risks in indian automotive manufacturing industries

In today’s VUCA – Volatile, Uncertain, Complex and Ambiguous environment, identifying and prioritizing risks critical to the entire supply chain of automotive manufacturing is imperative. Despite data availability, managers and decision-makers often face trouble while prioritizing risks under varying circumstances due to a lack of a structured approach. This research identifies risks faced by Indian automotive manufacturing industries through literature review and confirms after consulting industry experts. Further, this paper proposes an integrated approach of entropy weighted criteria and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) for ranking identified critical risk factors in the Indian automotive manufacturing industries. Adverse impact severity of thirteen identified risks on five different criteria “economic aspects”, “factory operations”, “company reputation”, “scheduling” and “quality of the end product/process” and considered during formulation decision matrix. Inputs from seven experts of leading Indian automotive industries were obtained for the overall ranking process. Among identified risks, “Delay Risks”, “Management Risks”, and “Supplier Risks” are ranked as the top three risks faced by Indian automotive manufacturing industries. The structured multi-criteria decision making (MCDM) approach for risks prioritization will guide managers and decision-makers while chalking out risk mitigation plans.

Recycling of composite waste by sequential application of multi-criteria decision-making, pyrolysis, and reproduction

This paper summarizes our work aimed at applying different methods sequentially in the evaluation of GFRP (glass fiber reinforced polyester) waste, which is a composite waste according to circular economy assumptions. For this aim, the management alternatives that are defined as mechanical recycling, chemical recycling, pyrolysis, and incineration methods are compared using the ANP (Analytic Network Process) and the VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) methods. To show the applicability of the pyrolysis, which was determined as the most appropriate method, experiments were carried out at different temperatures. After the general characterization of the products was conducted, the solid product was used to reproduce GFRP. After a tensile strength test of the GFRP, which was produced using fibers obtained from pyrolysis at 500°C, it was found that it had approximately 43.15% more tensile strength than polyester cast without fiber. Following a further oxidation step, the tensile strength increased by 61.50%. The liquid products have the potential to be used as fuel oil, and the gas product is sufficient to provide the energy requirements of a process plant. Pyrolysis proved to be a profoundly feasible method for not only recovering glass fiber, but also energy from GFRP waste.

Prioritization of roadblocks to adoption of industry 4.0 technologies in manufacturing industries using VIKOR

To experience seamless adoption of future industry 4.0, it is imperative to investigate its existing roadblocks and success factors in the Indian manufacturing context. Striking similarities among diverse researches highlight the importance of the cyber-physical system assuring many benefits to the manufacturing sector. Many studies concluded that sooner or later, all manufacturing companies will evolve by penetrating industry 4.0 technologies in their enterprise activities. There is a possibility of negative consequences on business functions if top management remains unprepared to assess roadblocks to these future technologies' adoption. Identification of roadblocks is the first and foremost thing industrial policymakers should focus on. This research enlists the stumbling blocks for industry 4.0 technologies' adoption that manufacturing industries may encounter by reviewing the latest literature and ranks them using VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methodology by consulting five experienced Indian industrial authorities. Weightage to each industrial experts' input is decided by the entropy method. Results indicate that “Skilled workers scarcity,” “Internal employee resistance,” “Worries about cyber security,” and “Inadequate knowledge of/from exterior agencies” are the top four barriers to the incorporation of industry 4.0 technologies in the Indian manufacturing context while “Lack of knowledge about enabling technology solutions” secured lower position. The robustness of the output was checked by carrying out a sensitivity analysis with varying weights. The approach adopted in this study will be helpful for industrial authorities to concentrate their efforts in tackling the most critical barriers. This research will aid industrial decision-makers in remaining proactive during the industrial technology adoption transition.

Benchmarking of deep learning algorithms for skin cancer detection based on a hybrid framework of entropy and VIKOR techniques

Skin cancer is one of the most common cancers worldwide caused by excessive development of skin cells. Considering the rapid growth of the use of deep learning algorithms for skin cancer detection, selecting the optimal algorithm has become crucial to determining the efficiency of computer-aided diagnosis (CAD) systems developed for the healthcare sector. However, a sufficient number of criteria and parameters must be considered when selecting an ideal deep learning algorithm. A generally accepted method for benchmarking deep learning models for skin cancer classification is unavailable in the current literature. This paper presents a multi-criteria decision-making framework for evaluating and benchmarking deep learning models for skin cancer detection based on hybridisation of entropy and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods. Twelve well-known convolution networks are evaluated and tested on eleven publicly available image datasets to achieve the target of the study. Several criteria related to deep convolutional neural networks (CNNs) architectures, including optimisation technique, transfer learning, class balancing, transfer learning, data augmentation, and network complexity, have been considered in the multi-criteria evaluation. The decision matrix (DM) is designed based on a crossover of the five evaluation criteria and twelve (CNNs) classification models on different datasets. Subsequently, in the benchmarking and ranking of deep learning classification models, multi-criteria decision making (MCDM) techniques are used. The MCDM uses a scheme that involves the integration of entropy with VIKOR approaches. For the weight calculations of evaluation criteria, entropy is applied, while VIKOR is used to benchmark and rank the models. The obtained results reveal that the InceptionResNetV2 model gained the first rank and is selected as the optimal architecture for skin cancer detection considering the five criteria investigated in our study. The presented framework achieves a significant performance in selecting the best algorithm, which could provide substantial guidance to the researcher working in the field.

Earthquake vulnerability in the Himalaya by integrated multi-criteria decision models

Himalayan mountains are one of the most seismo-tectonically active zones on the surface of the earth. Recurring moderate and high magnitude earthquakes are not uncommon in this region. This paper maps the earthquake vulnerability in the region using integrated multi-criteria decision models. Factors which may influence vulnerability in a region can be categorized in social, geotechnical, structural, and physical parameters. We have used the analytical hierarchy process (AHP) approach to determine the weights of various parameters, which were further used to develop earthquake vulnerability maps for the study area using the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Grey Relational Analysis (GRA) methods. There is good correlation between the vulnerability estimated through AHP-VIKOR and AHP-GRA methods. Our analysis indicates that more than 12% area may be under high to very-high vulnerability, whereas more than 44% population, and about 43% buildings are highly vulnerable to hazards due to earthquakes. The results would be useful for various hazard mitigation and infrastructure planning agencies working in the region.

An Integrated Pythagorean fuzzy soft computing approach to environmental management systems for sustainable energy pricing

This study aims to analyse environmental management systems for sustainable energy price. For this purpose, a new fuzzy multi-criteria decision-making (MCDM) model is generated. First, seven different criteria are defined with supported literature. After that, these factors are weighted by considering decision making trial and evaluation laboratory (DEMATEL) methodology based on 2-tuple hesitant interval-valued Pythagorean fuzzy sets (HIVPFs). Furthermore, different pricing strategies are also ranked by using HIVPF technique for order preference by similarity to ideal solution (TOPSIS) and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Also, a sensitivity analysis is also performed by considering six different conditions. The main contribution of this study is proposing a new mathematical model to solve a significant problem regarding energy prices. It is concluded that the results of both TOPSIS and VIKOR are quite similar. On the other side, it is also understood that the results of six different cases are almost the same. This situation explains that analysis results are reliable and coherent. The findings indicate that safety incidence and waste management are the most crucial factors for the sustainability of the energy price. In addition, skimming is the best pricing strategy for the energy industry. Within this framework, energy companies should firstly offer high prices to the market. After that, the price should be reduced when there is a decrease in the demand.

A Two Phase Integrated Fuzzy Decision-Making Framework for Green Supplier Selection in the Coffee Bean Supply Chain

In Vietnam, as more and more organizations are moving toward globalization, green supplier selection (GSS) has emerged as a strategic approach in supply chain management that requires supplier practices in lessening the environmental risks to society. Based on both conventional and environmental criteria, this paper aims to evaluate a set of suppliers by establishing a multi-criteria decision-making (MCDM)-based framework using an integrated fuzzy analytical hierarchy process (FAHP) with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Initially, five GSS criteria of the environmental aspect (wastewater treatment, solid waste generation, energy consumption, air pollution, corporate social responsibility) and conventional criteria (quality, cost, delivery, and technology) are identified from the literature and consulting field experts to employ the MCDM approach. The trustworthiness of the proposed integrated framework is presented by discussing a case study in the coffee bean supply chain in Vietnam. The FAHP is used to generate criteria weights in which fuzzy set theory is applied to translate the linguistic evaluation statements of experts, and VIKOR is used to rank the alternatives against the selected criteria. From FAHP findings, the most important criteria are quantity discount, solid waste generation, order fulfillment rate, logistics cost, and purchasing cost. A consistency test is performed to ensure the uniformity of the expert’s input. The best suppliers are determined through the final ranking of the VIKOR model for the case study. The work presented provides insight to decision-makers of supplier selection that helps determine significant GSS criteria and aids in the minimization of environmental risks to society arising from the supply chain on corporate sustainability standards.

A Comparative Adoption of COTS Database Components Using the Hybrid AHP-TOPSIS and AHP-VIKOR Methodologies

Objectives : This study aims at comparing two recent hybrid methodologies used to evaluate COTS database products based on reusability, including Analytic Hierarchy Process integrated with VIsekriterijumsko KOmpromisno Rangiranje (AHP-VIKOR), and Analytical Hierarchy Process integrated with Order Preference by Similarity to Ideal Solution (AHP-TOPSIS). Method: AHP is presented to determine the weight of each evaluation criterion. VIKOR and TOPSIS are used to acquire the final rank of software database alternatives. The evaluation process has been carried out using four database components, namely: Ingress, Oracle 9i, SQL Server 2005 and Microsoft Access. The focus is to compare the difference in the evaluation results between TOPSIS and VIKOR. Both methods are based on an aggregating function that represents closeness to the ideal solution. VIKOR is based on linear normalization whereas TOPSIS used vector normalization to eliminate the units of criterion functions. Findings: the solution obtained by TOPSIS method has the shortest distance from the ideal one and farthest from the negative ideal solution. VIKOR method helps to determine a compromise solution that gives a maximum group utility for the majority and minimum for opponents. The results show that the proposed multi-criterion decision-making approach can enhance objectivity the evaluation process of the given database alternatives. In addition, the comparison reveals the effectiveness, and weakness of each method. Keywords Commercial of the shelf (COTS), Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Analytical Hierarchy Process (AHP), Usability, Multiple Criteria Decision Making (MCDM), Normalizing weight, Database, Reuse

Assessment of safety management system on energy investment risk using house of quality based on hybrid stochastic interval-valued intuitionistic fuzzy decision-making approach

The purpose of the study is to construct a novel hybrid stochastic interval-valued intuitionistic fuzzy (IVIF) decision approach to the house of quality (HoQ)-based analysis of safety management system with the respect to the energy investment risks. For this purpose, two step analyses of IVIF decision making models by using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are applied for weighting and ranking criteria of energy investment risks and safety management system, respectively. Additionally, Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methodology is also taken into consideration to make a comparative evaluation. The novelty of study is to propose a new hybrid decision making model based on IVIF sets and to analyse the probability and non-probability degrees of the extreme values with the stochastic data comparatively for the robustness check. The results show that global economic uncertainty is the most important criterion while the changes in the policy and regulatory at the country level has relatively the weakest importance among the energy investment risk. Additionally, the competitive environment is defined as the most prominent factor of safety management system on the energy investment risk when the HoQ-based fuzzy matrix is applied. However, the analysis demonstrates that hybrid decision making approaches based on interval-valued fuzzy sets and stochastic values provide the coherent results for weighting the criteria of energy investment risks and ranking the factors of the safety management system with HoQ. Based on these results, it is understood that uncertainties in the global economy increase investors' future worries. When global economic uncertainties are high, the market must be effectively monitored, and the necessary actions should be taken immediately. For this purpose, the necessary training should be given to the personnel currently working in these companies. In this framework, staff-based training programs should be prepared according to the job descriptions of the employees. Additionally, regarding the new recruitment personnel process, qualified employees should be selected based on the job descriptions.

Comparative Analysis of Hybrid Fuzzy MCGDM Methodologies for Optimal Robot Selection Process

The generalized interval-valued trapezoidal fuzzy best-worst method (GITrF-BWM) provides more reliable and more consistent criteria weights for multiple criteria group decision making (MCGDM) problems. In this study, GITrF-BWM is integrated with the extended TOPSIS (technique for order preference by similarity to the ideal solution) and extended VIKOR (visekriterijumska optimizacija i kompromisno resenje) methods for the selection of the optimal industrial robot using fuzzy information. For a criteria-based selection process, assigning weights play a vital role and significantly affect the decision. Assigning weights based on direct opinions of decision makers can be biased, so weight deriving models, such as GITrF-BWM, overcome this discrepancy. In previous studies, generalized interval-valued trapezoidal fuzzy weights were not derived by using any MCGDM method for the robot selection process. For this study, both subjective and objective criteria are considered. The preferences of decision makers are provided with the help of linguistic terms that are then converted into fuzzy information. The stability and reliability of the methods were tested by performing sensitivity analysis, which showed that the ranking results of both the methodologies are not symmetrical, and the integration of GITrF-BWM with the extended TOPSIS method provides stable and reliable results as compared to the integration of GITrF-BWM with the extended VIKOR method. Hence, the proposed methodology provides robust optimal industrial robot selection.

Identifying the Strategic Priorities of the Technical Factors for the Sustainable Low Carbon Industry Based on Macroeconomic Conditions

This study aims to find out the strategic priorities of the technical factors to have sustainable low carbon industry. For this purpose, a hybrid multi-criteria decision-making (MCDM) is proposed that contains three different stages. First, the economic criteria required for sustainable development and the technical needs of the low carbon industry are defined by making a comprehensive literature review. After that, economic criteria are weighted by using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) methodology. Finally, technical factors are ranked with the help of the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach. Moreover, another analysis is also performed by considering fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to evaluate the consistency of the analysis results. The main motivation of this study is to define the primary technical factors to minimize carbon emission problem by proposing a hybrid MCDM model. The findings indicate that research and development for renewable sources has the greatest importance for low-carbon industry. In addition, the analysis results of fuzzy TOPSIS and fuzzy VIKOR are quite similar. This situation demonstrates the consistency and coherency of the ranking results. Hence, it is recommended that countries should mainly give importance to the research and development investments so that the costs of renewable energy problems can be minimized. This situation can attract the attentions of the companies to invest in these projects. In this way, it will be possible to use a cleaner energy in industrial production.

Using VIKOR with structural equation modeling for constructing benchmarks in the Internet industry

PurposeThe study uses VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)-structural equation modeling (SEM) to construct benchmarks for service providers and evaluate a multimethodology practice in the Internet industry.Design/methodology/approachThe data were collected using a survey based on an email/interview with 444 Internet users participating. SEM obtained coefficient values by way of customer expectations for Internet service providers. The authors normalized coefficients and integrated them into the VIKOR method for ranking competitors in the Internet industry.FindingsVIKOR-SEM revealed that network and information quality and security/privacy significantly positively impact customer trust while network and information quality and customer trust significantly positively affect Internet users' value perceptions. Customer services do not affect customer trust, while security/privacy and customer services have no significant influence on customer value perceptions. Though customer services and Internet users' trust and value perceptions directly, significantly and positively affect their commitments, the quality of network and information and security/privacy indirectly, significantly and positively influence customer commitments.Research limitations/implicationsThe study was in the Internet industry of Iraq. The results cannot be accurate for the other countries and the service sectors. The researchers/managers can adopt the model in other service sectors to test the multimethodology.Originality/valueVIKOR-SEM evaluated the changes in customer expectations and service providers in the Internet industry.

A hybrid fuzzy BWM-VIKOR MCDM to evaluate the service level of bike-sharing companies: A case study from Chengdu, China

Bike-sharing is being widely used in many cities in China and gradually penetrated people’s daily habits. However, because there are so many bicycle providers in the market, companies need to develop differentiated service strategies to compete in this homogenized market. Therefore, this paper examined the current bike-sharing enterprise service level in Chengdu, constructed a evaluation index system with 17 indicators under four main dimensions: perceptibility, availability, reliability, and sustainability; Developed a multi-stage hybrid fuzzy best and worst method (BWM) and Visekriterijumska Optimizacija i Kompromisno Resenje (VIKOR) method to evaluate bike-sharing service levels, understand user perceptions, and enable decision-makers to make more accurate and reliable judgments under uncertain conditions. This research provided a new comprehensive decision-making tool that the evaluation results could help managers improve their performances targeted, provide users with higher quality products and services, and contribute to the long-term development of companies.

A Hybrid Fuzzy Decision Model for Evaluating MEMS and IC Integration Technologies.

Integrated devices incorporating MEMS (microelectromechanical systems) with IC (integrated circuit) components have been becoming increasingly important in the era of IoT (Internet of Things). In this study, a hybrid fuzzy MCDM (multi-criteria decision making) model was proposed to effectively evaluate alternative technologies that incorporate MEMS with IC components. This model, composed of the fuzzy AHP (analytic hierarchy process) and fuzzy VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods, solves the decision problem of how best to rank MEMS and IC integration technologies in a fuzzy environment. The six important criteria and the major five alternative technologies associated with our research themes were explored through literature review and expert investigations. The priority weights of criteria were derived using fuzzy AHP. After that, fuzzy VIKOR was deployed to rank alternatives. The empirical results show that development schedule and manufacturing capability are the two most important criteria and 3D (three-dimensional) SiP (system-in-package) and monolithic SoC (system-on-chip) are the top two favored technologies. The proposed fuzzy decision model could serve as a reference for the future strategic evaluation and selection of MEMS and IC integration technologies.

Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection.

Industrial robots have different capabilities and specifications according to the required applications. It is becoming difficult to select a suitable robot for specific applications and requirements due to the availability of several types with different specifications of robots in the market. Best-worst method is a useful, highly consistent and reliable method to derive weights of criteria and it is worthy to integrate it with the evaluation based on distance from average solution (EDAS) method that is more applicable and needs fewer number of calculations as compared to other methods. An example is presented to show the validity and usability of the proposed methodology. Comparison of ranking results matches with the well-known distance-based approach, technique for order preference by similarity to ideal solution and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods showing the robustness of the best-worst EDAS hybrid method. Sensitivity analysis performed using eighty to one ratio shows that the proposed hybrid MCDM methodology is more stable and reliable.

New mHealth hospital selection framework supporting decentralised telemedicine architecture for outpatient cardiovascular disease-based integrated techniques: Haversine-GPS and AHP-VIKOR

Cardiovascular diseases (CVDs) are conditions involving the heart or blood vessels which need specialised and urgent care. Centralised telemedicine is a client–server architecture fit for cardiovascular care, especially for monitoring health conditions, delivering healthcare services and providing other remote services by using mHealth. However, several challenges and unsolved issues remain, including (1) provision of healthcare services data in terms of hospital connectivity and continuous updates of all transactions occurring across distributed hospital networks for patient data, (2) lack of an accurate mHealth method to estimate time between patients with CVD and telemedicine hospitals for hospitalisation and (3) lack of investigation of important criteria for hospital evaluation. To develop a new mHealth framework for the evaluation and prioritisation of decentralised telemedicine hospitals based on integrated techniques Haversine-Global Positioning System (GPS) and analytical hierarchy process (AHP)-VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The framework can serve all health emergency levels (i.e. risk, urgent and sick) of patients with CVD. Three methodology phases were developed. First is the identification of important decentralised hospital criteria which affect hospital evaluation to create a new dataset for this context. Second is the development of a new mHealth framework phase consequent of four development sequences: new integrated distance measurement through Haversine-based on GPS for time estimation for the convenient remote interaction with hospitals, combination for new hospital datasets and development of three decision matrixes based on a crossover of (1) healthcare service packages/time of arrival of patient at the hospital criteria and (2) lists of hospitals for evaluation and prioritisation using integration AHP-VIKOR techniques. Third is the objective validation of the constructed results. In addition, the proposed framework is evaluated by using a checklist benchmarking procedure. Experimental results reveal that the new mHealth framework is effective in decentralised telemedicine architecture and verify the ability of all connected hospitals. The new integrated distance measurement technique boosts the overall methodology of hospital evaluation and supports the combination of the new hospital datasets for prioritisation configuration. The proposed mHealth framework offers healthcare services for all emergence levels of patients with CVD through the blockchain concept and decision making theory. Objective validation reveals significant differences between the scores of groups, indicating that the ranking results are valid for the three decision matrices. Evaluation results show that the proposed mHealth framework exhibits an advantage over the benchmark frameworks with a percentage of 66.67% intersection with six comparison points highlighted by the academic literature.

Transition management theory-based policy framework for analyzing environmentally responsible freight transport practices

The freight transport industry of developing nations are trying very hard to achieve environmental sustainability in their logistics business. Meanwhile, it is essential to identify the transformation path towards sustainable freight transport for every nation. This paper has presented a framework based on transition management (TM) theory to identify, classify, and measure the important environmentally responsible transport practices (ERTPs). This work utilizes a two-step methodology comprised of a grey clustering (GC) algorithm and a compromise ranking method, i.e., VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) for prioritization of ERTPs performance. The GC algorithm is used to classify the ERTPs based on their impact on selected transport processes. Initially, this study identifies thirty-five ERTPs and four transport performance clusters. The proposed framework is validated with the Indian freight transport industry. The GC algorithm classifies twenty-one and fourteen practices as a high priority and moderate priority, respectively. The VIKOR method is used to compute the group utility, individual regret, and possibility value of high priority ERTPs. The ERTPs such as developing efficient freight terminal, compliance with government transport emission law and practice, promoting the use of the green container, environmental certification of freight carriers, and using alternative fuels are found as poor performing practices in the Indian freight transport industry. The proposed decision model robustness is tested with the sensitivity analysis, and a final ranking of the high priority ERTPs is presented. The outcomes of this research help logistics practitioners and policymakers in managing the ERTPs of freight logistics systems and develop a roadmap for improving freight transport sustainability.

Pain Treatment Evaluation in COVID-19 Patients with Hesitant Fuzzy Linguistic Multicriteria Decision-Making.

The coronavirus disease 2019 (COVID-19) has emerged as a worldwide pandemic since March 2020. Although most patients complain of moderate or severe pain, these symptoms are generally underestimated and appropriate treatment is not applied. This study aims to guide physicians in selecting and ranking various alternatives for the treatment of pain in COVID-19 patients. However, the choice of treatment for pain requires the consideration of many different conflicting criteria. Therefore, we have studied this problem as a multicriteria decision-making problem. Throughout the solution procedure, first, the criteria and subcriteria affecting the preferences are defined. Then, weight values are determined with respect to these criteria, as they have different degrees of importance for the problem. At this stage, hesitant fuzzy linguistic term sets (HFLTSs) are used, and thus, experts can convey their ideas more accurately. In this first phase of the study, an HFLTS integrated Analytic Hierarchy Process (AHP) method is utilized. Subsequently, possible treatment alternatives are evaluated by using the Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. According to the results obtained by considering expert evaluations, the most preferred treatment is the administration of paracetamol, followed by interventional treatments, opioids, and nonsteroidal anti-inflammatory drugs (NSAIDs), respectively. With this study, it is ensured that a more accurate method is followed by eliminating possible mistakes due to the subjective evaluations of experts in the process of determining pain treatment. This method can also be used in different patient and disease groups.

An integrated interval type-2 fuzzy technique for democratic–autocratic​ multi-criteria decision making

This paper develops an integrated trapezoidal interval type-2 fuzzy (TrIT2F) technique for democratic–autocratic multi-criteria group decision making based on best–worstmethod (BWM) and VIKOR (VIsekriterijumska optimizacija i KOm-promisno Resenje), which is called TrIT2F-BW-VIKOR. In this technique, the pairwise comparisons and evaluations are represented by trapezoidal interval type-2 fuzzy sets (TrIT2FSs). The existing definition of TrIT2FS is perfected by adding two rational constraints proposed in this paper. A weight-normalizing theorem is initiated to normalize the TrIT2F weights. To determine the TrIT2F weights of junior decision makers (JDMs) and criteria, the classical BWM is extended into TrIT2F environment, which is called TrIT2F-BWM. In this TrIT2F-BWM, the weight-normalizing theorem is applied to normalize the TrIT2F weights, a consistency ratio is designed to check the reliability of the obtained TrIT2F weights. Based on the determined weights of JDMs and criteria, an extended VIKOR is developed to rank alternatives. The proposed technique can not only effectively retain the inherent fuzzy information of TrIT2FSs, but also flexibly handle different decision situations. The validity of the proposed technique is demonstrated with a makeshift (fangcang) hospital selection example on COVID-19. Some sensitivity and comparison analyses are provided to show the stability, flexibility, and superiorities of the proposed technique.

Selection of Renewable Energy Alternatives for Green Blockchain Investments: A Hybrid IT2-based Fuzzy Modelling

In this study, it is aimed to determine the most suitable renewable energy alternatives that can be used in blockchain investments. In this context, firstly, a wide literature review is made and 6 different criteria that could have an impact on this decision are determined. The analysis process of the consists of two different stages. Firstly, the significance levels of these criteria are calculated with the help of interval type-2 (IT2F) decision making trial and evaluation laboratory (DEMATEL)-analytical hierarchy process (ANP) (DANP) method. According to the analysis results obtained, it has been determined that continuity in energy supply and legal conditions are the most important criteria. Hence, it is recommended that while choosing among renewable energy alternatives, it is necessary to pay attention to the legal regulations in the country. Another important point is that attention should be paid to this issue in renewable energy sources to be selected in order to have sustainable benefit from energy in blockchain technologies. On the other hand, in the second phase of the analysis process of the study, 5 different renewable energy alternatives are listed according to their suitability in blockchain technology. In this process, the IT2 fuzzy VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach has been taken into consideration. As a result, it is concluded that wind and solar energy are the most suitable energy alternatives for this technology. Considering the results obtained, it is understood that countries that use blockchain technology should pay particular attention to wind and solar investments. In this regard, companies that use wind and solar energy with individuals or institutions using blockchain technology should be in cooperation. Thanks to these renewable energy alternatives, excess energy consumption resulting from the use of blockchain technology can be achieved with environmentally friendly energy sources. In other words, it will be possible to minimize the carbon emission problem that occurs with the use of this technology.