Characterization of the productivity of a Mexican technology development company through fuzzy control

Authors

DOI:

https://doi.org/10.46661/revmetodoscuanteconempresa.5374

Keywords:

productivity, technology-based company, fuzzy logic, Monte Carlo simulation

Abstract

The development of a model that allows measuring the productivity of a technology-based company is presented, which is based on the interactions identified between the investment of the research department, computer fraud, and theft. These interactions are presented through a model of fuzzy variables in which the membership functions are developed for each of these. Likewise, the interaction rules are developed based on the conjunction of the fuzzy sets proposed for the Mamdani model. With these designs, it is possible to determine the degree of productivity, characterized by a fuzzy set. To test the model, Monte Carlo simulation was used with four scenarios. The simulations’ series results show that the fuzzy sets described allow to measure company’s productivity being analysed through the proposed fuzzy sets productivity ranges.

Downloads

Download data is not yet available.

References

Cabrera-Llanos, A.I., Ortiz-Arango, F., & Cruz-Aranda, F. (2019). Un Modelo de Minimización de Costos de Mantenimiento de Equipo Médico Mediante Lógica Difusa. Revista Mexicana de Economía y Finanzas, 14(3), 379-396. https://doi.org/10.21919/remef.v14i3.410.

CEPAL (2020). Acerca de Microempresas y Pymes. https://www.cepal.org/es/temas/pymes/acerca-microempresas-pymes.

Chattopadhyay, A.K., & Chattopadhyay, T. (2014). Monte Carlo Simulation. New York: Springer. http://link.springer.com/10.1007/978-1-4939-1507-1_10 (October 25, 2018).

Cheung, W.L., Pitcher, T.J., & Pauly, D. (2005). A Fuzzy Logic Expert System to Estimate Intrinsic Extinction Vulnerabilities of Marine Fishes to Fishing. Biological Conservation, 124(1), 97-111. https://doi.org/10.1016/j.biocon.2005.01.017

Dávila, G., Ortiz, F., & Cruz, F. (2016). Cálculo Del Valor En Riesgo Operacional Mediante Redes Bayesianas Para Una Empresa Financiera. Contaduria y Administracion, 61(1), 176-201. http://dx.doi.org/10.1016/j.cya.2015.09.009.

González, C. (2011). Lógica Difusa. Una introducción práctica. Técnicas de Softcomputing. Ed. Universidad de Castilla-La Mancha. https://www.esi.uclm.es/www/cglez/downloads/docencia/2011_Softcomputing/LogicaDifusa.pdf

Jamshidi, A., Yazdani-Chamzini, A., HajiYakhchali, S., & Khaleghi, S. (2013). Developing a New Fuzzy Inference System for Pipeline Risk Assessment. Journal of Loss Prevention in the Process Industries, 26(1), 197-208. https://doi.org/10.1016/j.jlp.2012.10.010

Kroese, D. (2011). Monte Carlo Methods. Lecture Notes. Department of Mathematics, School of Mathematics and Physics. The University of Queensland. https://github.com/dpkroese/Monte-Carlo-lecture-notes/blob/master/mccourse.pdf.

Passino, K.M. (1998). Fuzzy Control, Department of Electrical Engineering. USA: The Ohio State University. https://www2.ece.ohio-state.edu/~passino/FCbook.pdf

Sánchez, E.S. et al. (2015). Fuzzy-State Machine for Triage Priority Classifier in Emergency Room. In Springer, Cham, 1488–91. http://link.springer.com/10.1007/978-3-319-19387-8_361 (October 23, 2018).

Sivanandam, S.N., Sumathi, S., & Deepa, S.N. (2007). Introduction to Fuzzy Logic Using MATLAB. https://doi.org/10.1007/978-3-540-35781-0

Tah, J.H.M., & Carr, V. (2000). A Proposal for Construction Project Risk Assessment Using Fuzzy Logic. Construction Management and Economics, 18(4), 491-500. https://doi.org/10.1080/01446190050024905

Tejada, G. (2000). Tutorial de Lógica Fuzzy. Electrónica - UNMSM, 5, 18-29. https://revistasinvestigacion.unmsm.edu.pe/index.php/electron/article/view/4426

Vicent, I. (2014). Conjuntos difusos: aplicación al control de procesos. Prácticas Externas y Proyecto Final de Grado en Matemática Computacional. Universidad de Valencia.

Published

2022-12-01

How to Cite

Cabrera Llanos, A. I., Ortiz Arango, F., & Dávila Aragón, G. (2022). Characterization of the productivity of a Mexican technology development company through fuzzy control. Journal of Quantitative Methods for Economics and Business Administration, 34, 281–304. https://doi.org/10.46661/revmetodoscuanteconempresa.5374

Issue

Section

Articles