Bonferroni means with induced ordered weighted average operators

Pythagorean Membership Grade Aggregation Operators: Application in Financial knowledge

This paper presents the Pythagorean membership grade induced ordered weighted moving average (PMGIOWMA) operator with some particular cases and theorems. The main advantage of this new operator is that can include the knowledge, expectation, and aptitude of the decision maker into the Pythagorean membership function by using a weighting vector and induced variables. An application in financial knowledge based on a survey conducted in 13 provinces in Boyacá, Colombia, is presented.

The Ordered Weighted Average Human Development Index

The main aim is to propose a new method for estimating the Human Development Index using ordered weighted average. To develop this method, ordered weighted geometric average (OWGA), induced OWGA prioritized OWA (POWA) operator are studied. Using Human Development Index formulation in combination to aggregations operators presented above is proposed the prioritized induced ordered weighted geometric average (PIOWGA) operator. A mathematical application is carried out to estimate the Human Development Index and compare it with the traditional method and other existing methods. Finally, it is noted that decision makers have an influence on the order given in the ranking by its attitude and criterion, and method can capture the subjective information prioritized by them.

Predicting the future price of a commodity using the OWMA operator: An approximation of the interest rate and inflation in the brown pastusa potato price

This main aim of this paper is to propose a methodology for the prediction of the future price of the brown pastusa potato in Colombia, taking into consideration the variables of interest rate, as measured by fixed term deposits (FTDs), and inflation rate, as measured by the consumer price index (CPI). The methodology conducts linear regression analysis and assesses the results using the significance test, the Durbin-Watson statistic, analysis of the variance inflation factor (VIF) and the coefficient of determination. After that, the forecast of the independent variables has been conducted with the ordered weighted moving average (OWMA) operator and new proposed OWA operators using probabilities that are presented in the paper. Using these new methods and the proposed econometric model, it is possible to establish future prices. The results show a greater impact of the interest rate than inflation, as well as the need to include supply and demand variables that have not been included due to the absence of systematic information.

The ordered weighted government transparency average: Colombia case

The main aim of this paper is to propose a new aggregation operator to improve the evaluation of the transparency index. This new operator is called the prioritized induced ordered weighted average weighted average (PIOWAWA) operator. The main characteristics of the PIOWAWA operator are that it allows considering the degree of importance, reordering and weight factors given to the information in the same formulation by the decision maker. A mathematical application is performed using a Colombia transparency case. The findings highlight that according to the operator used, there are significant changes in the ranking. The main implications are given by using these aggregation operators for the generation of scenarios by considering the changes in the allocation of weights, the level of importance and the ordering of information simultaneously.

Bonferroni Prioritized Aggregation Operators Applied to Government Transparency

This article applies the Bonferroni prioritized induced heavy ordered weighted average (OWA) to analyze a series of data and focuses on the Bonferroni average and heavy induced prioritized aggregation operators. The objective of the present work is to present a new aggregation operator that combines the heavy induced prioritized Bonferroni and its formulations and represents the Bonferroni mean with variables that induce an order with vectors that are greater than one. This work develops some extensions using prioritization. The main advantage is that different types of information provided by a group of decision makers to compare real situations are included in this formulation. Finally, an example using the operators to calculate the transparency of the websites of the 32 states of Mexico was performed. The main idea was to visualize how the ranking can change depending on the importance of the five components of the methodology. The main results show that it is possible to detect some important changes depending on the operator and the experts considered.

Bonferroni Probabilistic Ordered Weighted Averaging Operators Applied to Agricultural Commodities’ Price Analysis

Financial markets have been characterized in recent years by their uncertainty and volatility. The price of assets is always changing so that the decisions made by consumers, producers, and governments about different products is not still accurate. In this situation, it is necessary to generate models that allow the incorporation of the knowledge and expectations of the markets and thus include in the results obtained not only the historical information, but also the present and future information. The present article introduces a new extension of the ordered weighted averaging (OWA) operator called the Bonferroni probabilistic ordered weighted average (B-POWA) operator. This operator is designed to unify in a single formulation the interrelation of the values given in a data set by the Bonferroni means and a weighted and probabilistic vector that models the attitudinal character, expectations, and knowledge of the decision-maker of a problem. The paper also studies the main characteristics and some families of the B-POWA operator. An illustrative example is also proposed to analyze the mathematical process of the operator. Finally, an application to corn price estimation designed to calculate the error between the price of an agricultural commodity using the B-POWA operator and a leading global market company is presented. The results show that the proposed operator exhibits a better general performance than the traditional methods.

Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology

Existing methods for forecasting the productivity of a factory are subject to a major drawback—the lower and upper bounds of productivity are usually determined by a few extreme cases, which unacceptably widens the productivity range. To address this drawback, an interval fuzzy number (IFN)-based mixed binary quadratic programming (MBQP)–ordered weighted average (OWA) approach is proposed in this study for modeling an uncertain productivity learning process. In the proposed methodology, the productivity range is divided into the inner and outer sections, which correspond to the lower and upper membership functions of an IFN-based fuzzy productivity forecast, respectively. In this manner, all actual values are included in the outer section, whereas most of the values are included within the inner section to fulfill different managerial purposes. According to the percentages of outlier cases, a suitable forecasting strategy can be selected. To derive the values of parameters in the IFN-based fuzzy productivity learning model, an MBQP model is proposed and optimized. Subsequently, according to the selected forecasting strategy, the OWA method is applied to defuzzify a fuzzy productivity forecast. The proposed methodology has been applied to the real case of a dynamic random access memory factory to evaluate its effectiveness. The experimental results indicate that the proposed methodology was superior to several existing methods, especially in terms of mean absolute error, mean absolute percentage error, and root mean square error in evaluating the forecasting accuracy. The forecasting precision achieved using the proposed methodology was also satisfactory.

Picture Fuzzy Interaction Partitioned Heronian Aggregation Operators for Hotel Selection

Picture fuzzy numbers (PFNs), as the generalization of fuzzy sets, are good at fully expressing decision makers’ opinions with four membership degrees. Since aggregation operators are simple but powerful tools, this study aims to explore some aggregation operators with PFNs to solve practical decision-making problems. First, new operational rules, the interaction operations of PFNs, are defined to overcome the drawbacks of existing operations. Considering that interrelationships may exist only in part of criteria, rather than all of the criteria in reality, the partitioned Heronian aggregation operator is modified with PFNs to deal with this condition. Then, desirable properties are proved and several special cases are discussed. New decision-making methods with these presented aggregation operators are suggested to process hotel selection issues. Last, their practicability and merits are certified by sensitivity analyses and comparison analyses with other existing approaches. The results indicate that our methods are feasible to address such situations where criteria interact in the same part, but are independent from each other at different parts.