Prioritization of healthcare systems during pandemics using Cronbach's measure based fuzzy WASPAS approach

Pandemics are well-known as epidemics that spread globally and cause many illnesses and mortality. Because of globalization, the accelerated occurrence and circulation of new microbes, the infection has emerged and the incidence and movement of new microbes have sped up. Using technological devices to minimize the visit durations, specifying days for handling chronic diseases, subsidy for the staff are the alternatives that can help prevent healthcare systems from collapsing during pandemics. The study aims to define the efficient usage of optimization tools during pandemics to prevent healthcare systems from collapsing. In this study, a new integrated framework with fuzzy information is developed, which attempts to prioritize these alternatives for policymakers. First, rating data are assigned respective fuzzy values using the standard singleton grades. Later, criteria weights are determined by extending Cronbach´s measure to fuzzy context. The measure not only understands data consistency comprehensively, but also takes into consideration the attitudinal characteristics of experts. By this approach, a rational weight vector is obtained for decision-making. Further, an improved Weighted Aggregated Sum Product Assessment (WASPAS) algorithm is put forward for ranking alternatives, which is flexibly considering criteria along with personalized ordering and holistic ordering alternatives. The usefulness of the developed framework is tested with the help of a real case study. Rank values of alternatives when unbiased weights are used is given by 0.741, 0.582, 0.640 with ordering as R1≻R3≻R2. The sensitivity/comparative analysis reveals the impact of the proposed model as useful in selecting the best alternative for the healthcare systems during pandemics

Interval-valued probabilistic hesitant fuzzy set-based framework for group decision-making with unknown weight information

This paper aims at presenting a new decision framework under an interval-valued probabilistic hesitant fuzzy set (IVPHFS) context with fully unknown weight information. At first, the weights of the attributes are determined by using the interval-valued probabilistic hesitant deviation method. Later, the DMs’ weights are determined by using a recently proposed evidence theory-based Bayesian approximation method under the IVPHFS context. The preferences are aggregated by using a newly extended generalized Maclaurin symmetric mean operator under the IVPHFS context. Further, the alternatives are prioritized by using an interval-valued probabilistic hesitant complex proportional assessment method. From the proposed framework, the following significances are inferred; for example, it uses a generalized preference structure that provides ease and flexibility to the decision-makers (DMs) during preference elicitation; weights are calculated systematically to mitigate inaccuracies and subjective randomness; interrelationship among attributes are effectively captured; and alternatives are prioritized from different angles by properly considering the nature of the attributes. Finally, the applicability of the framework is validated by using green supplier selection for a leading bakery company, and from the comparison, it is observed that the framework is useful, practical and systematic for rational decision-making and robust and consistent from sensitivity analysis of weights and Spearman correlation of rank values, respectively

Solving renewable energy source selection problems using a q-rung orthopair fuzzy-based integrated decision-making approach

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

Scientific Decision Framework for Evaluation of Renewable Energy Sources under Q-Rung Orthopair Fuzzy Set with Partially Known Weight Information

As an attractive generalization of the intuitionistic fuzzy set (IFS), q-rung orthopair fuzzy set (q-ROFS) provides the decision makers (DMs) with a wide window for preference elicitation. Previous studies on q-ROFS indicate that there is an urge for a decision framework which can make use of the available information in a proper manner for making rational decisions. Motivated by the superiority of q-ROFS, in this paper, a new decision framework is proposed, which provides scientific methods for multi-attribute group decision-making (MAGDM). Initially, a programming model is developed for calculating weights of attributes with the help of partially known information. Later, another programming model is developed for determining the weights of DMs with the help of partially known information. Preferences from different DMs are aggregated rationally by using the weights of DMs and extending generalized Maclaurin symmetric mean (GMSM) operator to q-ROFS, which can properly capture the interrelationship among attributes. Further, complex proportional assessment (COPRAS) method is extended to q-ROFS for prioritization of objects by using attributes’ weight vector and aggregated preference matrix. The applicability of the proposed framework is demonstrated by using a renewable energy source prioritization problem from an Indian perspective. Finally, the superiorities and weaknesses of the framework are discussed in comparison with state-of-the-art methods

A Scientific Decision Framework for Supplier Selection under Interval Valued Intuitionistic Fuzzy Environment

This paper proposes a new scientific decision framework (SDF) under interval valued intuitionistic fuzzy (IVIF) environment for supplier selection (SS). The framework consists of two phases, where, in the first phase, criteria weights are estimated in a sensible manner using newly proposed IVIF based statistical variance (SV) method and, in the second phase, the suitable supplier is selected using ELECTRE (ELimination and Choice Expressing REality) ranking method under IVIF environment. This method involves three categories of outranking, namely, strong, moderate, and weak. Previous studies on ELECTRE ranking reveal that scholars have only used two categories of outranking, namely, strong and weak, in the formulation of IVIF based ELECTRE, which eventually aggravates fuzziness and vagueness in decision making process due to the potential loss of information. Motivated by this challenge, third outranking category, called moderate, is proposed, which considerably reduces the loss of information by improving checks to the concordance and discordance matrices. Thus, in this paper, IVIF-ELECTRE (IVIFE) method is presented and popular TOPSIS method is integrated with IVIFE for obtaining a linear ranking. Finally, the practicality of the proposed framework is demonstrated using SS example and the strength of proposed SDF is realized by comparing the framework with other similar methods

A decision framework under probabilistic hesitant fuzzy environment with probability estimation for multi-criteria decision making

With growing hesitation in human perception, hesitant fuzzy set, an important extension of fuzzy set, has gained much attention from the research community. The concept of HFS gives decision makers the ability to provide multiple preferences for the same instance. However, the chance of these preferences occurring is assumed to be equal, which is unreasonable in practice. To circumvent this issue, probabilistic hesitant fuzzy set (PHFS) is adopted in this work, which is an extension of hesitant fuzzy set with associated probability values. Based on the literature review on PHFS, it is evident that (i) occurrence probability of each element was not methodically calculated; (ii) hesitation was not properly captured during criteria weight calculation; (iii) interrelationship among criteria was not captured during aggregation; and (iv) broad/rational ranking of alternatives with compromise solution was lacking. Motivated by these challenges and to alleviate the same, a systematic procedure is proposed in this paper to estimate these probabilities. Additionally, in this procedure, decision makers’ preferences are aggregated using the newly proposed probabilistic hesitant fuzzy generalized Maclaurin symmetric mean operator and criteria weights are calculated using the proposed statistical variance method in the context of PHFS. A new ranking method is also proposed that extends a well-known VIKOR method to the PHFS context. Further, the practical use of the proposed decision framework is demonstrated by two examples viz., selecting a suitable coordinator for a research and development project and selection of a doctoral candidate for the supervisor position. Finally, the strength and weakness of the proposed decision framework are realized by comparing it with state-of-the-art methods

Selection of Apt Renewable Energy Source for Smart Cities using Generalized Orthopair Fuzzy Information

Renewable energy (RE) is a popular and clean source of energy that could potentially reduce carbon footprint and promote sustainable development in smart cities. Developing countries, such as India, have invested time, money, and effort into the proper development of smart cities. As there are different RE alternatives and several criteria used for its selection, researchers have adopted multi-criteria decisionmaking methods for systematic selection. Previous studies on RE selection did not (i) handle uncertainty effectively; (ii) calculate experts' weights systematically, and (iii) consider interdependencies among experts during aggregation. Motivated by these lacunas, this paper develops a new decision framework. The framework utilizes generalized orthopair fuzzy information, which is flexible and provides rich scope for handling uncertainty. Additionally, a regret theory-based weight calculation method is proposed for systematic weight calculation. Finally, Score-based Muirhead mean is proposed for aggregation of preferences and ranking of REs. An actual case study in Tamil Nadu is presented to exemplify the usefulness of the framework. Comparison with extant models reveals the superiorities of the framework

Assessment of cloud vendors using interval-valued probabilistic linguistic information and unknown weights

Cloud vendors (CVs) play an indispensable role in the development of IT sectors and industry 4.0. Many CVs evolve every day, and a systematic selection of these is becoming substantial for organizations. Literature studies have shown that multicriteria decision-making (MCDM) is a powerful tool for systematic selection. However, the major issue with the state-of-the-art models is that they do not effectively represent uncertainty. Moreover, the personalized selection of CVs based on user queries is not prominent in an MCDM context. In this paper, to circumvent these issues, a new decision framework is proposed that utilizes a generalized preference style called interval-valued probabilistic linguistic term set (IVPLTS). This preference style considers occurring probability values as interval numbers instead of a single precise value, which provides flexibility during preference elicitation. Initially, missing values are imputed systematically by using a case-based method. Then, the consistency of these preferences is checked using Cronbach's alpha coefficient, and the inconsistent preferences are repaired rationally by using an iterative method. A programming model is proposed for determining the weights of the evaluation criteria. Furthermore, Maclaurin symmetric mean (MSM) is extended to IVPLTS for aggregating preferences from each expert. The interval-valued probabilistic linguistic comprehensive (IVPLC) method is proposed for prioritizing CVs in a personalized manner. Finally, the framework's practicality is validated by using a case study of CV selection for an academic institution; strengths and weaknesses of the framework are conferred by comparison with extant CV selection models