Due to apparent flexibility of Intuitionistic Fuzzy Set (IFS) concepts in dealing with the imprecision or uncertainty, these are proving to be quite useful in many application areas for a more human consistent reasoning under imperfectly defined facts and imprecise knowledge. In this paper, we apply notions of entropy and intuitionistic fuzzy sets to present a new fuzzy decision-making approach called intuitionistic fuzzy entropy measure for selection and ranking the suppliers with respect to the attributes. An entropy-based model is formulated and applied to a real case study aiming to examine the rankings of suppliers. Furthermore, the weights for each alternative, with respect to the criteria, are calculated using intuitionistic fuzzy entropy measure. The supplier with the highest weight is selected as the best alternative. This proposed model helps the decision-makers in better understanding of the weight of each criterion without relying on the mere expertise.

Credibility measures in vague environments are to quantify the approximate chance of occurrence of fuzzy events. This paper presents a novel definition about credibility for intuitionistic fuzzy variables. We axiomatize this credibility measure and to clarify, give some examples. Based on the notion of these concepts, we provide expected values, entropy, and general formulae for the central moments and discuss them through examples.

Experimental designs are commonly used to produce valid, defensible, and supportable conclusions. Among commonly used block designs, the class of Latin square designs is used to study factors or treatment levels expressed as Latin letters and applying two blocking factors in rows and columns to simultaneously control two sources of nuisance variability. Another block design in which the error can be controlled by blocking three nuisance factors is obtained by simply using two superimposed Latin square designs, with one using the Latin letters and the other using the Greek letters. Such a design is termed as a Graeco-Latin square (GLS) design. While observing or measuring data in field or lab experiments, it is often noted to have vague, incomplete, and imprecise data for whatsoever reasons. In this regard, researchers have proposed various emerging approaches, which are based on fuzzy, intuitionistic fuzzy, and neutrosophic logic, and provide deeper understanding, analysis, and interpretations of the data. In this paper, we provide a brief review of the history of GLS designs and propose a neutrosophic Graeco-Latin square design, its model, and the analysis. To illustrate this, we have considered an experimental study which analyzes the effects of different formulations of a rocket propellant, which are used in aircrew escape systems, on the observed burning rate.

Natural gas, a cleanest fossil fuel, has become more and more popular in recent periods because of its being used in commercial, industrial, power generation and residential applications. Because of its uses and economic implications, Natural gas prices are affected primarily by the market supply and demand functions. Factors like production, imports and storage inventory levels on the supply side influence gas prices. Increases in supply results in gas prices down while decreases in supply tend to push gas prices up. Increased gas prices tend to encourage increased gas production, imports and sales while declining gas prices result in the opposite effects. Factors, like weather, temperature, economic conditions and petroleum prices, play a key role in increases/decreases in demand. Our aim in this study is to analyze the Canadian and US natural gas markets, and for the purpose of prediction, to fit appropriate models to establish the functional relationship between Natural gas prices and covariates like energy demand and supply and economic growth indicators. We evaluate the statements of codependence among various cofactors contributing to gas prices. We consider a novel methodology of modeling the functional relationship by applying various piecewise polynomials and study the efficiency by carrying the prediction error analysis.