Bapi Dutta

Bio: Bapi Dutta is an academic researcher from National University of Singapore. The author has contributed to research in topics: Computer science & Fuzzy number. The author has an hindex of 12, co-authored 30 publications receiving 412 citations. Previous affiliations of Bapi Dutta include Indian Institute of Technology Patna & Indian Institutes of Technology.

Partitioned Bonferroni mean based on linguistic 2-tuple for dealing with multi-attribute group decision making

Abstract: Graphical abstractDisplay Omitted HighlightsThe proposed method presents an attempt to model a new kind of interrelationship structure among the attributesThe concept of partitioned Bonferroni mean is introduced based on linguistic informationA novel method is proposed to determine the weight vector of the decision makers In this study, a multi-attribute group decision making (MAGDM) problem is investigated, in which decision makers provide their preferences over alternatives by using linguistic 2-tuple In the process of decision making, we introduce the idea of a specific structure in the attribute set We assume that attributes are partitioned into several classes and members of intra-partition are interrelated while no interrelationship exists among inter partition We emphasize the importance of having an aggregation operator, to capture the expressed inter-relationship structure among the attributes, which we will refer to as partition Bonferroni mean (PBM) We also investigate the behavior of the proposed PBM operator Further to aggregate the given linguistic information to get overall performance value of each alternative in MAGDM, we analyze PBM operator in linguistic 2-tuple environment and develop three new linguistic aggregation operators: 2-tuple linguistic PBM (2TLPBM), weighted 2-tuple linguistic PBM (W2TLPBM) and linguistic weighted 2-tuple linguistic PBM (LW-2TLPBM) Based on the idea that total linguistic deviation between individual decision maker's opinions and group opinion should be minimized, we develop an approach to determine weight of the decision makers Finally, a practical example is presented to illustrate the proposed method and comparison analysis demonstrates applicability of the proposed method

Weight computation of criteria in a decision-making problem by knowledge measure with intuitionistic fuzzy set and interval-valued intuitionistic fuzzy set

Abstract: The aim of this study is to propose an objective method for determining weights of criteria (also called attributes) based on a new measure of intuitionistic fuzzy information, called knowledge measure, in a real-world multi-criteria decision-making problem under intuitionistic fuzzy and interval-valued intuitionistic fuzzy environment. To address this issue, we first analyze the existing entropy measures and show that their use in objective weight determination process may lead us to produce unreliable weights of criteria by citing appropriate examples. Then we analyze important properties of knowledge measure of intuitionistic fuzzy set (IFS) and also define knowledge measure for interval-valued intuitionistic fuzzy set. Then a new method to determine the weights of criteria is developed on the basis of knowledge measure where information about criteria weights is completely unknown and partly known. A real-life example is presented to illustrate the proposed weight determination method and a comparative analysis is carried out to indicate the practicality and effectiveness of knowledge-based weight-generation method under both intuitionistic fuzzy and interval-valued intuitionistic fuzzy environment. Finally, we formulate the axioms for knowledge measure associated with IFSs and we also propose families (classes) of knowledge measures.

A Model Based on Linguistic 2-Tuples for Dealing With Heterogeneous Relationship Among Attributes in Multi-expert Decision Making

Abstract: Classical Bonferroni mean, defined by Bonferroni in 1950, assumes homogeneous relation among the attributes, i.e., each attribute A is related to the rest of the attributes A \ {A i }, where A = {A 1 , A 2 , ...,A n } denotes the attribute set. In this paper, we emphasize the importance of having an aggregation operator, which we will refer to as the extended Bonferroni mean (EBM) operator to capture heterogeneous interrelationship among the attributes. We provide an interpretation of “heterogeneous interrelationship” by assuming that some of the attributes, which are denoted as A , are related to a subset B of the set A \ {A i }, and others have no relation with the remaining attributes. We provide an interpretation of this operator as computing different aggregated values for a given set of inputs as interrelationship pattern is changed. We also investigate the behavior of the proposed EBM aggregation operator. Furthermore, to investigate a multiattribute group decision making (MAGDM) problem with linguistic information, we analyze the proposed EBM operator in linguistic 2-tuple environment and develop three new linguistic aggregation operators: 2-tuple linguistic EBM, weighted 2-tuple linguistic EBM, and linguistic weighted 2-tuple linguistic EBM. A concept of linguistic similarity measure of 2-tuple linguistic information is introduced. Subsequently, an MAGDM technique is developed, in which the attributes' weights are in the form of 2-tuple linguistic information and experts' weights information is completely unknown. Finally, a practical example is presented to demonstrate the applicability of our results.

Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic

Abstract: The objective of the present study is to develop/establish a web-based medical diagnostic support system (MDSS) by which health care support can be provided for people living in rural areas of a country. In this respect, this research provides a novel approach for medical diagnosis driven by integrating fuzzy and intuitionistic fuzzy (IF) frameworks. Subsequently, based on the proposed approach a web-based MDSS is developed. The proposed MDSS comprises of a knowledge base (KB) and intuitionistic fuzzy inference system (IFIS). Based on the observation that medical data cannot be described with both precision and certainty, a medical KB is constructed in the form of a set of if-then decision rules by employing both fuzzy and IF logics. After constructing the medical KB, a new set of patients is considered for diagnosing the diseases. For each patient, linguistic values of the patients' symptoms are considered as inputs of the proposed IFIS and modeled by using the generalized triangular membership functions. Subsequently, integrated fuzzy and IF rule-based inference system is used to find a valid conclusion for the new set of patients. In a nutshell, in this paper fuzzy rule-based and IFS based inference systems are combined for better and more realistic representation of uncertainty of the medical diagnosis problem and for more accurate diagnostic result. The method is composed of following four steps: (1) the modeling of antecedent part of the rules, which consist of linguistic assessments of the patients' symptoms provided by the doctors/medical experts with their corresponding confidence levels, by using generalized fuzzy numbers; (2) the modeling of consequent part, which reveals the degree of association and the degree of non-association of diseases into the patient, by using IFSs; (3) the use of IF aggregation operator in inference process; (4) the application of relative closeness function to find the final crisp output for a given diagnosis. Finally, the applicability of the proposed approach is illustrated with a suitable case study. This article has also justified the proposed approach by using similarity measurement.

Comprehensive minimum cost models for large scale group decision making with consistent fuzzy preference relations

Abstract: Nowadays, society demands group decision making (GDM) problems that require the participation of a large number of experts, so-called large scale group decision making (LS-GDM) problems. Logically, the more experts are involved in the decision making process, the more common is the emergence of disagreements in the group. For this reason, consensus reaching processes (CRPs) are key in the resolution of these problems in order to smooth such disagreements in the group and reach consensual solutions. A CRP requires that experts are receptive to change their initial preferences, but demanding excessive changes could lead to deadlocks. The well-known minimum cost consensus (MCC) model allows to obtain an agreed solution by preserving experts’ preferences as much as possible. However, this MCC model only considers the distance among experts and collective opinion, which is not enough to guarantee a desired degree of consensus. To overcome this limitation, it was proposed comprehensive MCC models (CMCC) in which both consensus degree and distance are considered, and CMCC models deal with fuzzy preference relations (FPRs) for modeling experts’ opinions. However, these models are not efficient to deal with LS-GDM problems and the FPRs consistency is ignored in them. Therefore, this paper aims to propose new CMCC models focused on LS-GDM problems in which experts use FPRs whose consistency is taken into account in order to obtain reliable results. A case study is introduced to show the effectiveness of the proposed models.

A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package

Abstract: The application of multi-attribute utility theory whose aggregation process is based on the Choquet integral requires the prior identification of a capacity. The main approaches to capacity identification proposed in the literature are reviewed and their advantages and inconveniences are discussed. All the reviewed methods have been implemented within the Kappalab R package. Their application is illustrated on a detailed example.

Pairwise comparison matrix in multiple criteria decision making

Abstract: The measurement scales, consistency index, inconsistency issues, missing judgment estimation and priority derivation methods have been extensively studied in the pairwise comparison matrix (PCM). Various approaches have been proposed to handle these problems, and made great contributions to the decision making. This paper reviews the literature of the main developments of the PCM. There are plenty of literature related to these issues, thus we mainly focus on the literature published in 37 peer reviewed international journals from 2010 to 2015 (searched via ISI Web of science). We attempt to analyze and classify these literatures so as to find the current hot research topics and research techniques in the PCM, and point out the future directions on the PCM. It is hoped that this paper will provide a comprehensive literature review on PCM, and act as informative summary of the main developments of the PCM for the researchers for their future research. First published online: 02 Sep 2016

Multi-attribute group decision-making under probabilistic uncertain linguistic environment

Abstract: Existing decision-making methods cannot work under the probabilistic uncertain linguistic environment where the decision makers give different uncertain linguistic terms as their assessments and the weights of assessments are different. In this paper, a novel concept called probabilistic uncertain linguistic term set is proposed, which is composed of some possible uncertain linguistic terms associated with the corresponding probabilities. Then, the normalization process, comparison method, basic operations, and aggregation operators are studied for probabilistic uncertain linguistic term sets. After that, an extended technique for order preference by similarity to an ideal solution method and an aggregation-based method are developed to rank the alternatives and then select the best one for multi-attribute group decision-making with probabilistic uncertain linguistic information. Finally, a practical case concerning the selection of Cloud storage services is shown to illustrate the applicability o...