Arunodaya Raj Mishra

Bio: Arunodaya Raj Mishra is an academic researcher from Government College. The author has contributed to research in topics: Computer science & Fuzzy logic. The author has an hindex of 23, co-authored 74 publications receiving 1386 citations. Previous affiliations of Arunodaya Raj Mishra include ITM University & Jaypee University of Engineering and Technology.

A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India

Abstract: 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.

A novel approach to extended fuzzy TOPSIS based on new divergence measures for renewable energy sources selection

Abstract: In recent years, the selection of appropriate renewable energy sources is an extremely significant issue that affects the environmental development and economic growth. To tackle the concern, various authors have concentrated on preferring desirable energy source(s) adopting decision-making approaches under different fuzzy sets methods. In this regard, in the present study, a new divergence measure is proposed for ranking and choosing the renewable energy sources in multi-criteria decision-making problems based on fuzzy TOPSIS, and it is compared to some existing methods. Then, a set of experts related to renewable energy sources is selected to evaluate possible alternatives amongst conflicting criteria. Moreover, the fuzzy decision matrix and criteria weights are measured using linguistic values that are transformed into fuzzy values. Furthermore, the weight of each energy source decision expert is evaluated by the proposed method. Next, the importance of criteria is computed by an extensive maximizing deviation method inspired by fuzzy divergence measure. Finally, the problem of choosing a renewable energy source is considered to show the thorough execution process of the introduced method. The proposed method’s strength lies in its capability of providing effective solutions where there is a shortage of quantitative information.

A novel hesitant fuzzy WASPAS method for assessment of green supplier problem based on exponential information measures

Abstract: The main goal of green supply chain management is to minimize the injurious ecological impacts in all activities and phases of a supply chain. Evaluating the suppliers and selecting the best one based on environmental criteria can facilitate us to reach the objective of green supply chain management. As the assessment generally consists of various alternatives over different criteria, green supplier selection is regarded as a multi-criteria decision-making problem. Hesitant fuzzy set, which is an extension of fuzzy set, is an effective tool to handle the vagueness in such a way that hesitant and flawed information by allowing the degree of belongingness for a green supplier selection over the evaluation criteria. In this paper, an integrated method is developed based on Weighted Aggregated Sum Product Assessment (WASPAS) approach to solve the multi-criteria decision-making problems with hesitant fuzzy information. This method is based on hesitant fuzzy operators, some improvement in the conventional WASPAS approach and a procedure for calculating the criteria weights. To calculate the criteria and decision expert weights, we propose new information measures for hesitant fuzzy sets and combine entropy and divergence measures for criteria weights, while we use similarity measure for decision expert weights. Since the uncertainty is an inevitable feature of multi-criteria decision-making problems, the developed approach can be a useful tool for uncertain multi-criteria decision-making atmosphere. Next, a green supplier selection problem is taken to show the usefulness of the developed approach in real-life decision-making problems. The results of this study found that the most significant criteria for green supplier selection were management commitment (0.3119), environmental management system (0.2259) and green product (0.2010). Also, we demonstrate a sensitivity analysis over different parameter values and sets of criteria weight to illustrate the stability of the developed method. Finally, the outcome developed approach with existing approaches is compared to validate the developed method.

A novel extended approach under hesitant fuzzy sets to design a framework for assessing the key challenges of digital health interventions adoption during the COVID-19 outbreak

Abstract: In recent years, Digital Technologies (DTs) are becoming an inseparable part of human lives. Thus, many scholars have conducted research to develop new tools and applications. Processing information, usually in the form of binary code, is the main task in DTs, which is happening through many devices, including computers, smartphones, robots, and applications. Surprisingly, the role of DTs has been highlighted in people's life due to the COVID-19 pandemic. There are several different challenges to implement and intervene in DTs during the COVID-19 outbreak; therefore, the present study extended a new fuzzy approach under Hesitant Fuzzy Set (HFS) approach using Stepwise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) method to evaluate and rank the critical challenges of DTs intervention to control the COVID-19 outbreak. In this regard, a comprehensive survey using literature and in-depth interviews have been carried out to identify the challenges under the SWOT (Strengths, Weaknesses, Opportunities, Threats) framework. Moreover, the SWARA procedure is applied to analyze and assess the challenges to DTs intervention during the COVID-19 outbreak, and the WASPAS approach is utilized to rank the DTs under hesitant fuzzy sets. Further, to demonstrate the efficacy and practicability of the developed framework, an illustrative case study has been analyzed. The results of this study found that Health Information Systems (HIS) was ranked as the first factor among other factors followed by a lack of digital knowledge, digital stratification, economic interventions, lack of reliable data, and cost inefficiency In conclusion, to confirm the steadiness and strength of the proposed framework, the obtained outputs are compared with other methods.

A novel EDAS approach on intuitionistic fuzzy set for assessment of health-care waste disposal technology using new parametric divergence measures

Abstract: Nowadays, health-care waste management is a challenging issue for both the public and government sectors because it contains infectious, radioactive, or hazardous waste. Selecting a suitable method for health-care waste disposal is known as a complex decision-making problem due to the existence of several multiple criteria that may in conflict with each other. In this paper, we propose a novel method called evaluation based on distance from average solution framework based on parametric divergence measures with the context of intuitionistic fuzzy sets to evaluate and rank the health-care waste disposal alternative. To do this, first, novel parametric intuitionistic fuzzy divergence measures are developed, and various desired properties have also been examined. Second, the decision experts are evaluated, and the criteria weights are computed by developed parametric divergence measure method. Third, the preference order of the alternatives is illustrated by the developed framework. The proposed framework is based on the positive and negative distances from the average solution. Moreover, the health-care waste disposal alternative selection problem is considered to elucidate the applicability of the proposed framework. Four health-care waste disposal alternatives, including incineration, steam sterilization, microwave and landfill disposal, are considered in this study. The health-care waste disposal alternatives considered in this study include. The outcome illustrates that, the steam sterilization was the highest appraisal score (0.7025) and therefore, it should be chosen as the most suitable health-care waste disposal method in this study. Also, a comparison with existing approaches is shown to illustrate the validity and practicability of the developed framework.

Sustainable tourism or maintainable tourism: Managing resources for more than average outcomes

Abstract: This article explores the concept of sustainable tourism and in particular the nexus between maintainable tourism and sustainable tourism. It argues that the nexus involves an understanding of stakeholder perceptions, and applies this to the Daintree region of Far North Queensland, Australia, to determine whether tourism in the region is operating in a sustainable or maintainable manner. In order to do this, an iterative approach was taken and local people, operators, regulators and tourists were interviewed, and content analysis applied to management and strategic documents for the region. The results illustrate the importance of understanding stakeholder perceptions in facilitating sustainable tourism.

On some distance measures of complex Pythagorean fuzzy sets and their applications in pattern recognition

Abstract: The concept of complex fuzzy set (CFS) and complex intuitionistic fuzzy set (CIFS) is two recent developments in the field of fuzzy set (FS) theory. The significance of these concepts lies in the fact that these concepts assigned membership grades from unit circle in plane, i.e., in the form of a complex number instead from [0, 1] interval. CFS cannot deal with information of yes and no type, while CIFS works only for a limited range of values. To deal with these kinds of problems, in this article, the concept of complex Pythagorean fuzzy set (CPFS) is developed. The novelty of CPFS lies in its larger range comparative to CFS and CIFS which is demonstrated numerically. It is discussed how a CFS and CIFS could be CPFS but not conversely. We investigated the very basic concepts of CPFSs and studied their properties. Furthermore, some distance measures for CPFSs are developed and their characteristics are studied. The viability of the proposed new distance measures in a building material recognition problem is also discussed. Finally, a comparative study of the proposed new work is established with pre-existing study and some advantages of CPFS are discussed over CFS and CIFS.

A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making

Abstract: Energy policy making is one of the most significant issues for countries and it can be evaluated by using multi-criteria decision making (MCDM) methods. The energy decision and policy-making problems include selecting among energy alternatives, evaluating energy supply technologies, determining energy policy and energy planning. There is a wide range of studies about energy decision-making problems in the literature and different types of energy alternatives are considered in these studies. The MCDM methods are used as effective tools in order to solve energy decision-making problems since they evaluate alternatives with different perspectives in terms of several conflicting criteria. In this context, the fuzzy set theory (FST) that expresses uncertainties in human opinions, can be successfully used together with the MCDM methods to get more sensitive, concrete and realistic results. This paper aims to present a comprehensive review and bring together existing literature and the most recent advances to lead researchers about the methodologies and applications of fuzzy MCDM in the energy field. For this aim, a large number of papers that use fuzzy MCDM methods to solve energy policy and decision making problems have been analyzed with respect to some characteristics such as types of fuzzy sets, year, journal, fuzzy MCDM method, country and document type. The results of this study indicate that fuzzy Analytic Hierarchy Process (AHP), as an individual tool or by integrating with another MCDM method, is the most applied MCDM method and type-1 fuzzy sets are the most preferred type of fuzzy sets. Additionally, Turkey and China are countries which have the highest number of publications related to fuzzy MCDM methods in energy-related problems.