There are several models of uncertainty found in the literature like fuzzy set, rough set, soft set and hesitant fuzzy set. Also, several hybrid models have come up as a combination of these models and have been found to be more useful than the individual models. In everyday life we make many decisions. Making efficient decisions under uncertainty needs better techniques. Many such techniques have been developed in the recent past. These techniques involve soft sets and fuzzy sets. In this paper we redefined the hesitant fuzzy soft sets (HFSS) with the help of membership function. We also provide a decision making algorithm. © Springer Nature Singapore Pte Ltd. 2017.

Balakrushna Tripathy

Department of Analytics

School of Computer Science and Engineering

Vellore Campus

Sooraj T.R

Mohanty R.K

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Hesitant Fuzzy Soft Set Theory and Its Application in Decision Making

Advances in Intelligent Systems and Computing Artificial Intelligence and Evolutionary Computations in Engineering Systems

As seed selection is a challenging task due to the presence of hundreds of varieties of seeds of each kind, some homework is necessary for selecting suitable seeds as new varieties and kinds of seeds are introduced in the market every year having their own strengths and weaknesses. The complexities involved in the characteristics in the form of parameters results in uncertainties and as a result some uncertainty based model or hybrid models of more than is required to model the scenario and come out with a decision. Soft sets have enough of parameterization tools to support and hence is the most suitable one for such a study. However, as hybrid models are more efficient, the authors select a model called the interval valued fuzzy soft set (IVFSS) and propose a decision-making algorithm for the selection of seeds. A real database of seeds is used for experimental verification of the efficiency of the algorithm. This is the first attempt for such a study. The use of signed priorities and intervals for the membership of values for entities makes the study more efficient and realistic.

Fulltext

International Journal of Fuzzy System Applications

An Interval Valued Fuzzy Soft Set Based Optimization Algorithm for High Yielding Seed Selection

There are several models of uncertainty found in the literature like fuzzy set (FS), rough set, intuitionistic fuzzy set, soft set, and hesitant fuzzy set. Also, several hybrid models have come up as a combination of these models and have been found to be more useful than the individual models. In everyday life, we make many decisions. Making efficient decisions under uncertainty needs better techniques. Many such techniques have been developed in the recent past. These techniques involve soft sets (SS) and intuitionistic fuzzy sets. It is well known that intuitionistic hesitant fuzzy sets are more general than intuitionistic fuzzy sets. In this paper, we redefine interval-valued intuitionistic hesitant fuzzy soft sets (IVIHFSS) and also propose a decision-making technique which extends some of the recently developed algorithms. We also provide an application from real-life situations which illustrates the working of the algorithm and its efficiency over the other algorithms. © Springer Nature Singapore Pte Ltd. 2018.

Smart Computing and Informatics Smart Innovation, Systems and Technologies

A New Approach to Interval-Valued Intuitionistic Hesitant Fuzzy Soft Sets and Their Application in Decision Making

Decision making has become a common feature in day-to-day activities. Uncertainty-based models are more efficient in handling such problems. In this chapter, we propose an algorithm in this direction by using a hybrid model formed by combining the two models of soft set (SS) and the intuitionistic fuzzy set (IFS). We follow the characteristic function approach used by Tripathy et al. for the purpose. The illustrative real-life example shows the efficiency of our algorithm over other such models. © Springer Nature Singapore Pte Ltd. 2018.

Improved Decision Making Through IFSS

Many models handle uncertainty problems. Fuzzy set (FS) is one of them. The next one is intuitionistic fuzzy set (IFS). Further generalization is interval-valued fuzzy set (IVFS). But all those models had some difficulty due to lack of parameterization tool, which motivated mathematician Molodtsov to introduce soft set model in 1999. Hybrid models of these models are more efficient. Interval-valued intuitionistic fuzzy soft set (IVIFSS) introduced by Jiyang. Following their characteristic function approach Tripathy et al. introduced fuzzy soft set as a hybrid model in 2015. Here, we continue this further to define IVI fuzzy soft sets (IVIFSS). Many related concepts like complement, null, and absolute IVIFSS are introduced and operations like intersection and union of IVIFSSs are also redefined. Recently, soft set is applied in various forms to derive decision-making (DM) by Tripathy et al. We extend it further by proposing an algorithm which uses IVIFSS in order to achieve DM. These algorithms are much improved and applicable than that of Jiyang. Also, it generalizes all the previous algorithms in this direction. © Springer Science+Business Media Singapore 2017.

Proceedings of the International Conference on Data Engineering and Communication Technology Advances in Intelligent Systems and Computing

IVIFS and Decision-Making

Soft sets were introduced by Molodtsov to handle uncertainty based problems. In 2003, Maji et al. extended the soft set approach to decision making. Decision making plays an integral role in daily life. In this chapter we describe soft sets and its impact in decision making problems. In this chapter, we discuss some of the basic notions of fuzzy soft sets and interval valued fuzzy soft sets. We also discuss application of fuzzy soft sets and interval valued fuzzy soft sets in decision making. In this chapter, we are going to see how parameter reduction is used in soft set theory.

Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making Advances in Business Strategy and Competitive Advantage

Parameter Reduction in Soft Set Models and Application in Decision Making

Soft set (SS) theory was introduced by Molodtsov to handle uncertainty. It uses a family of subsets associated with each parameter. Hybrid models have been found to be more useful than the individual components. Earlier interval-valued fuzzy set (IVFS) was introduced as an extension of fuzzy set (FS) by Zadeh. Yang introduced the concept of IVFSS by combining and soft set models. Here, we define IVFSS through the membership function approach to define soft set by Tripathy et al. very recently. Several concepts, such as complement of an IVFSS, null IVFSS, absolute IVFSS, intersection, and union of two IVFSSs, are redefined. To illustrate the application of IVFSSs, a decision-making (DM) algorithm using this notion is proposed and illustrated through an example. © Springer Science+Business Media Singapore 2017.

Advances in Intelligent Systems and Computing Advances in Computational Intelligence

A New Approach to Interval-Valued Fuzzy Soft Sets and Its Application in Decision-Making

Soft set theory introduced by Molodtsov is a new mathematical approach to handle the uncertainty problems. It is a family of subsets associated with each parameter in a soft space. Hybrid models have been found to be more useful than the individual components. Earlier fuzzy set and soft set were combined to form fuzzy soft sets and similarly intuitionistic fuzzy soft sets (IFSS) were introduced. Like the soft sets, IFSS is also a notion which allows fuzziness over a soft set model. So far, many attempts have been made to define this concept. Maji et.al defined intuitionistic fuzzy soft sets and several operations on them. Following the definition of soft sets provided by Tripathy et.al (2015) through characteristic function, in this paper we improve the group decision algorithm proposed by Tripathy et al earlier and provide an application in handling the decision making problem. © 2016 IEEE.

2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS)

On intuitionistic fuzzy soft set and its application in group decision making

Soft set theory was introduced by Molodtsov to handle uncertainty. It uses a family of subsets associated with each parameter. Hybrid models have been found to be more useful than the individual components. Earlier fuzzy set and soft set were combined to form fuzzy soft sets (FSS). Soft sets were defined from a different point of view in Tripathy et al. (Int J Reasoning-Based Intell Syst 7(3/4), 224–253, 2015) where they used the notion of characteristic functions. Hence, many related concepts were also redefined. In Tripathy et al. (Proceedings of ICCIDM-2015, 2015) membership function for FSSs was defined. We propose a new algorithm by following this approach which provides an application of FSSs in group decision making. The performance of this algorithm is substantially improved than that of the earlier algorithm. © Springer Science+Business Media Singapore 2016.

Journal	Data powered by TypesetAdvances in Intelligent Systems and Computing Artificial Intelligence and Evolutionary Computations in Engineering Systems
Publisher	Data powered by TypesetSpringer Singapore
ISSN	2194-5357
Open Access	0