Research Outputs
Toma de Decisiones Estratégicas en Entornos Inciertos
El proceso de toma de decisiones tiene una incidencia relevante en los resultados de las empresas, lo que ha llevado a desarrollar novedosos métodos que permitan evaluar bajo condiciones no controlables elementos subjetivos y racionales. En ese sentido, el objetivo principal de este trabajo estudia los operadores de agregación en la toma de decisiones en entornos inciertos. Se presentan dos metodologías que permiten agregar información, que se llaman operadores OWA y BON-OWA. La aplicación de estos operadores se realiza en la selección de lanzamiento de nuevos productos. La principal ventaja de estos operadores es que permiten capturar la actitud del decisor y la comparación e interrelación continua de la información. Así, se destaca el análisis holístico que ofrecen estos métodos sobre la toma de decisiones en incertidumbre, que permite integrar conceptos de la teoría administrativa y la teoría de la agregación en un caso aplicado, visualizando como la inclusión de la información genera cambios dentro de los rankings de selección de alternativas.
Series of floor and ceiling functions—Part II: Infinite series
In this part of a series of two papers, we extend the theorems discussed in Part I for infinite series. We then use these theorems to develop distinct novel results involving the Hurwitz zeta function, Riemann zeta function, polylogarithms and Fibonacci numbers. In continuation, we obtain some zeros of the newly developed zeta functions and explain their behaviour using plots in complex plane. Furthermore, we provide particular cases for the theorems and corollaries that show that our results generalise the currently available functions and series such as the Riemann zeta function and the geometric series. Finally, we provide four miscellaneous examples to showcase the vast scope of the developed theorems and showcase that these two theorems can provide hundreds of new results and thus can potentially create an entirely new field under the realm of number theory and analysis.
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.
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.
Multiple criteria hierarchy approach for analyzing the competitiveness of regions in Mexico
The present paper has the main aim to evaluate the competitive level of the regions of Mexico based on their performance on 10 main factors from 100 indicators. The methodology is based on the Multiple Criteria Hierarchy Process (MCHP) capable of analysing the performance of a subset and the comprehensive indicators, and how they impact the competitiveness of the region. An important aspect of the MCHP implemented is that it considers the interaction between criteria (indicators) and measure the performance of a large number of criteria. The main contribution to the research is with the identification of region with the worst level of competitiveness, and the factors are requiring more attention by the decision-makers.
Using the ordered weighted average operator to gauge variation in agriculture commodities in India
Agricultural product prices are subject to various uncertainties, including unpredictable weather conditions, pest infestations, and market fluctuations, which can significantly impact agricultural yields and productivity. Accurately assessing and understanding price is crucial for farmers, policymakers, and stakeholders in the agricultural sector to make informed decisions and implement appropriate risk management strategies. This study used the ordered weighted average (OWA) operator and its extensions as mathematical aggregation techniques incorporating ordered weights to capture and evaluate the factors influencing price variation. By generating different vectors related to different inputs to the traditional formulation, it is possible to aggregate information to calculate and provide a new view of the outcomes. The results of this research can help enhance risk management practices in agriculture and support decision-making processes to mitigate the adverse effects of price.
Fuzzy analysis of the strategic actions of travel agencies in Boyacá, Colombia, in a Post-COVID-19 era
The economic impact of COVID-19 is undeniable, and one of the sectors most affected by this situation was tourism; when departures were canceled and what is known as “The Great Lockdown” began, the activity of this sector was paralyzed. In this regard, knowing which strategic actions must be implemented in order to recover economically is vital. This study aims to identify the importance of the strategic actions of travel agencies in Boyacá following the COVID-19 crisis using aggregation operators and fuzzy techniques. The methodology uses the experton method, Bonferroni’s OWAAC method, maximum similarity sub-relationships and Pichat’s algorithm, and the relative incidence analysis method to determine the importance of the actions taken. The findings show that most managers’ implemented strategic actions, including highlighting financial capacity and marketing (improvement actions and establishing alliances), which were the strategic actions with the highest incidence. These actions identify a focus for activities to reactivate the business and are related to the company’s routine operations.
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.
Sustainable development goals analysis with ordered weighted average operators
The present research proposes a new method to analyze the sustainable development goals (SDGs) index using ordered weighted average (OWA) operators. To develop this method, five experts evaluated and designated the relative importance of each of the 17 SDGs defined by the United Nations (UN), and with the use of the OWA and prioritized OWA (POWA) operators, rankings were generated. With the results, it is possible to visualize that the ranking of countries can change depending on the weights related to each SDG because the OWA and POWA operator methods can capture the uncertainty of the phenomenon.
Personnel Selection in a Coffee Shop Company Based on a Multi-Criteria Decision-Aiding and Artificial Intelligence Approach
Human capital management is a strategic element for companies in a globalized world. Therefore, they must use strategies and methods to recruit and select personnel assertively to focus their training, strengthening, and business growth efforts. Personnel selection can be seen as a decision problem and can be addressed in a multi-criteria decision-making context. This work aims to present the selection process of a barista in a Mexican coffee shop. The baristas could be the face of the company to customers, and they could significantly impact their overall experience. The personnel selection process included eleven candidates and three criteria. This process was performed using the ELECTRE-III to model the preferences of a decision-maker and RP2-NSGA-II+H, a multi-objective evolutionary algorithm that exploits fuzzy outranking relations to derive multi-criteria rankings. The ordering obtained with the algorithm did not have any inconsistency concerning the integral preference model, and it allowed for the selection of a candidate to occupy the barista position. The results show the relevance of combining preference modeling with multi-criteria analysis methods for decision-making and artificial intelligence techniques.