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The development opportunities and high-performance capacity of offshore wind energy project depends on the selection of the suitable offshore wind power station (OWPS) location. The present study aims to introduce a decision-making model for assessing the locations for OWPS from multiple criteria and uncertainty perspectives. In this regard, the concept of interval-valued intuitionistic fuzzy set (IVIFS) is utilized to express uncertain information. To quantify the degree of difference between IVIFSs, an improved distance measure is proposed and further utilized for deriving the objective weights of criteria. Numerical examples are discussed to illustrate the usefulness of introduced IVIF-distance measure. The RANking COMparison (RANCOM) based on interval-valued intuitionistic fuzzy information is presented to determine the subjective weights of criteria. With the combination of objective and subjective weights of criteria, an integrated weighting tool is presented to find the numeric weights of criteria under IVIFS environment. Further, a hybrid interval-valued intuitionistic fuzzy Weighted integrated Sum Product (WISP) approach is developed to prioritize the OWPS locations from multiple criteria and uncertainty perspectives. This approach combines the benefits of two normalization tools and four utility measures, which approves the effect of beneficial and non-beneficial criteria by means of weighted sum and weighted product measures. Further, the developed approach is applied to the OWPS location selection problem of Gujarat, India. Sensitivity and comparative analyses are presented to confirm the robustness and stability of the present WISP approach. This study provides an innovative decision analysis framework, which makes a significant contribution to the OWPS locations assessment problem under uncertain environment. [ABSTRACT FROM AUTHOR]

Sustainable supplier selection (SSS) is recognized as a prime aim in supply chain because of its impression on profitability, adorability, and agility of the organization. This work introduces a multi-phase intuitionistic fuzzy preference-based model with which decision experts are authorized to choose the suitable supplier using the sustainability "triple bottom line (TBL)" attributes. To solve this issue, an intuitionistic fuzzy gained and lost dominance score (IF-GLDS) approach is proposed using the developed IF-entropy. To make better use of experts' knowledge and fully represent the uncertain information, the evaluations of SSS are characterized in the form of intuitionistic fuzzy set (IFS). To better distinguish fuzziness of IFSs, new entropy for assessing criteria weights is proposed with the help of an improved score function. By considering the developed entropy and improved score function, a weight-determining process for considered criterion is presented. A case study concerning the iron and steel industry in India for assessing and ranking the SSS is taken to demonstrate the practicability of the developed model. The efficacy of the developed model is certified with the comparison by diverse extant models. [ABSTRACT FROM AUTHOR]

Suitable management and treatment of healthcare waste (HCW) have become a key issue due to its potential risk to human health and the environment, predominantly in emerging nations. The selection of an optimal HCW treatment (HCWT) option is a complicated multicriteria decision-making (MCDM) problem that includes several incompatible qualitative and quantitative attributes. This article presents an extended MCDM methodology for assessing and choosing the HCWT options using Pythagorean fuzzy stepwise weight assessment ratio analysis (PF-SWARA) and additive ratio assessment (PF-ARAS) approaches. To do this, attribute weights are estimated by the SWARA model and the ARAS framework decides the preference order of the options on Pythagorean fuzzy sets (PFSs). Furthermore, a selection problem of HCWT options in India is presented within PFSs to illustrate the efficiency and practicality of the introduced framework. Comparative discussions and sensitivity analysis are presented to demonstrate the rationality and stability of the developed approach for prioritizing HCWT options. [ABSTRACT FROM AUTHOR]

The role of cloud services in the data-intensive industry is indispensable. Cision recently reported that the cloud market would grow to 55 billion USD, with an active contribution of the cloud to healthcare around 2025. Inspired by the report, cloud vendors expand their market and the quality of services to seek growth globally. The rapid growth of the cloud sector in the healthcare industry imposes a challenge: making a rational choice of a cloud vendor (CV) out of a diverse set of vendors. Typically, the healthcare industry 4.0 sees the issue as a large-scale group decision-making problem. Previous studies on a CV selection face certain challenges, such as (i) a lack of the ability to handle multiple users' views, as well as experts'/users' complex linguistic views; (ii) the confidence level associated with a view is not considered; (iii) the transformation of multiple users' views into holistic data is lacking; and (iv) the systematic prioritization of CVs with minimum human intervention is a crucial task. Motivated by these challenges and circumventing them, a new big data-driven decision model is put forward in this paper. Initially, the data in the form of complex expressions are collected from multiple cloud users and are further transformed into a holistic decision matrix by adopting probabilistic linguistic information (PLI). PLI represents complex linguistic expressions along with the associated confidence levels. Later, a holistic decision matrix is formed with the missing values imputed by proposing an imputation algorithm. Furthermore, the criteria weights are determined by using a newly proposed mathematical model and partial information. Finally, the evaluation based on the distance from average solution (EDAS) approach is extended to PLI for the rational ranking of CVs. A real-time example of a CV selection for a healthcare center in India is exemplified so as to demonstrate the usefulness of the model, and the comparison reveals the merits and limitations of the model. [ABSTRACT FROM AUTHOR]

Sustainable site selection for electric vehicle charging station (EVCS) is a significant process in the promotion of electric vehicle system development. The assessment and selection of suitable EVCS site is a very critical decision, involving complexity due to the presence of several associated criteria. Furthermore, uncertainty is an inevitable component of the information in the decision‐making procedure and its significance in the selection process is relatively high and needs to be cautiously measured. Single‐valued neutrosophic set (SVNS) is one of the valuable and flexible tools for handling such type of uncertain information arising in multi‐criteria decision‐making (MCDM) applications. Thus, the objective of this study is to introduce novel single‐valued neutrosophic information‐based additive ratio assessment (ARAS) approach for evaluating and prioritizing the sustainable EVCS sites. In this method, novel single‐valued subjective and objective weighted integrated approach (SVN‐SOWIA) is developed to compute the criteria by aggregating the objective weights resulted from a similarity measure‐based procedure and the subjective weights given by the experts. For this purpose, an innovative similarity measure is proposed for SVNSs. To display the performance of the present methodology, a computational study of EVCS sites evaluation is conferred under single‐valued neutrosophic environment. Comparative and sensitivity analyses are further performed to verify the strength of the developed approach. The outcome illustrates EVCS site EvUrjaa—Electric Vehicle Charging Station is the most optimal EVCS site in Indore region, India. Also, the environmental (0.324) and social (0.273) criteria are more important than technological (0.236) and economical (0.167) criteria in assessing the EVCS sites. The sensitivity analysis outcomes signify the EVCS option EvUrjaa—Electric Vehicle Charging Station always acquires its highest ranking in spite of how sub‐criteria weights fluctuate. The outcome of this study indicates that the developed approach can suggest more realistic performance under uncertain environment and therefore, provides a wide range of applications. [ABSTRACT FROM AUTHOR]

Over two decades, telecommunication market has been continuing a huge growth in urban, semi-urban and rural regions of India. Investigating and choosing the desirable cellular mobile telephone service providers (TSPs) based on operative constraint can facilitate to obtain the ideal of TSPs selection. Here, the evaluation commonly encompasses different TSP options under various operational factors; thus, the assessment of TSPs can be considered as a rigorous multi-criteria decision-making (MCDM) issue. This paper initiates a structure for the exploration of the TSPs in the Madhya Pradesh region, India. To do this, a framework associated with grey relational analysis (GRA) on intuitionistic fuzzy sets (IFSs) is planned to obtain the performance of various telecom participants. The developed approach is based on the conception of the best and worst solutions. To find the attribute weights, a divergence measure is developed and employed by relative comparisons. Additionally, to show the proficiency and practicality, a selection problem of TSPs options of Madhya Pradesh circle, India is presented within the IFSs context. By employing the developed model, an expert can enlarge tactics to enhance the performance by standard operational factors. Comparison and sensitivity analysis are considered to validate the developed approach in the prioritization of the TSPs options. [ABSTRACT FROM AUTHOR]

The utilization of renewable energy sources or technologies has grown huge attention from the last few decades. The selection of renewable energy technologies is a laborious task for decision-makers. Therefore, the present study develops a new method using novel divergence and entropy measures of Pythagorean Fuzzy Sets (PFSs) and the Vlsekriterijumska Optimizacija I KOmpromisno Resenje (VIKOR) to evaluate renewable energy technologies. Recently, several studies have been presented regarding PFSs. However, there is very less investigation about the entropy measure of PFSs, particularly there is no study presented for a divergence measure of PFSs in the available literature. Consequently, this paper firstly develops novel information measures for PFSs. Next, the VIKOR method is extended to solve The Multiple-Criteria Decision Making (MCDM) problems with PFSs which the information of criteria weights is completely unidentified. To achieve the significant degrees of the Decision-Experts (DEs) and the criteria, new approaches are determined with the help of two developed measures including Pythagorean fuzzy-entropy and Pythagorean fuzzy-divergence. Moreover, an operation is utilized to PF weighted averaging operator for integrating the individual decision information into a group decision matrix. Further, the proposed PF-divergence measure is implemented to determine the particular measure of closeness of the alternatives in the present method. Finally, to exemplify the efficiency of the proposed approach, a selection problem of renewable energy technologies is presented where the evaluation of the energy alternatives versus each criterion is expressed in terms of Pythagorean Fuzzy Numbers (PFNs). The results of this study demonstrated that the proposed PF-VIKOR was effective to select and evaluate renewable energy technologies. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated decision-making framework for the systematic selection of a renewable energy source (RES) from a set of RESs based on sustainability attributes. A real case study of RES selection in Karnataka, India, using the framework is demonstrated, and the results are compared with state-of-the-art methods. The main reason for developing this framework is to handle uncertainty and vagueness effectively by reducing human intervention. Systematic selection of RESs also reduces inaccuracies and promotes rational decision-making. In this paper, q-rung orthopair fuzzy information is adopted to minimize subjective randomness by providing a flexible and generalized preference style. Further, the study found systematic approaches for imputing missing values, calculating attributes' and decision-makers' weights, aggregation or preferences, and prioritizing RESs, which are integrated into the framework.. Comparing the proposed framework with state-of-the-art-methods shows that (i) biomass and solar are suitable RESs for the process under consideration in Karnataka, (ii) the proposed framework is consistent with state-of-the-art methods, (iii) the proposed framework is sufficiently stable even after weights of attributes and decision makers are altered, and (iv) the proposed framework produces broad and sensible rank values for efficient backup management. These results validate the significance of the proposed framework. • Missing values are imputed systematically in decision matrices. • Hesitation of decision makers is captured during attribute weight calculation. • Decision makers' weights are calculated rationally for aggregation. • Preferences are aggregated by capturing interdependencies among decision makers. • q-rung orthopair fuzzy TODIM is proposed for renewable energy evaluation. [ABSTRACT FROM AUTHOR]