Person:
Peña Ortega, Mario Patricio

Birth Date

1987-12-26

ORCID

0000-0002-3986-7707

Scopus Author ID

57202190504

Afiliación

Universidad de Cuenca, Cuenca, Ecuador
Universidad de Cuenca, Facultad de Ciencias Químicas, Cuenca, Ecuador

País

Ecuador

Organizational Units

Organizational Unit

Facultad de Ciencias Químicas

Fundada en 1955 como la Escuela de Química Industrial, la facultad ha sido un pilar fundamental en la formación de profesionales altamente capacitados, comprometidos con el desarrollo de la ciencia, la educación y el bienestar social. La Facultad de Ciencias Químicas pone a consideración su trabajo académico, investigativo y de vinculación con la sociedad, desarrollado a través de la práctica de una docencia de calidad, investigación e innovación en su área de estudio. Desde su oficio de conocimiento se permite contribuir a la sociedad con cuatro carreras: Bioquímica y Farmacia, Ingeniería Química, Ingeniería Ambiental e Ingeniería Industrial. Su carta de presentación en la Academia, la coloca como una dependencia dinámica, donde confluye la solidez de una trayectoria de más de sesenta años. Aquí se trabaja en una continua formación de pregrado y posgrado de la más alta calidad, mediante la mejora continua con la innovación y a la vanguardia de las ciencias químicas.

Job Title

Profesor (T)

Last Name

Peña Ortega

First Name

Mario Patricio

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Now showing 1 - 10 of 28

A hybrid algorithm for supply chain optimization of assembly companies
(IEEE, 2019) Cevallos Tapia, Carlos Patricio; Sigüenza Guzmán, Lorena Catalina; Peña Ortega, Mario Patricio; Peña Ortega, Mario Patricio
A fundamental goal of any system is to get an optimal state. These optimal states can be found in different areas, such as medicine, engineering, or architecture. In the field of industrial engineering, one of its objectives is improving or optimizing company processes in order to increase benefits while reducing costs. In this context, an essential component is the supply chain, which is a network in that different entities, such as manufacturers, suppliers, distributors, retailers, transporters, and customers or end-users, are associated. Several optimization algorithms with different approaches have been developed to optimize the supply chain. Nevertheless, they still have problems to fulfill some requirements at once. This research aims to develop a hybrid optimization algorithm that leverages the capabilities of different approaches. This algorithm, which presents a multi-objective optimization schema, meets a tradeoff between the optimization results quality and the runtime. To this end, a manufacturing and assembly company is used as a case study to prove the algorithm. The results are also compared with other state-of-the-art algorithms using the same execution environment and general settings. Findings indicate that the hybrid algorithm converges in less time and in most cases, it could reach the global optimal.
New hybrid algorithm for supply chain optimization
(Springer, Cham, 2021) Cevallos Tapia, Carlos Patricio; Peña Ortega, Mario Patricio; Sigüenza Guzmán, Lorena Catalina; Sigüenza Guzmán, Lorena Catalina
Optimization is the process of obtaining the best solutions to specific problems. In the literature, those problems have been optimized through a plethora of algorithms. However, these algorithms have many advantages but also disadvantages. In this article, a New Hybrid Algorithm for Supply Chain Optimization, NHA-SCO has been proposed in order to improve the benefits of objective function convergence. For the analysis of the results, three assembly companies have been utilized as case studies. These companies present their supply chains, i.e., networks where products flow from their raw material to the final products delivered to clients. These supply chains must satisfy different objectives, such as maximize benefits and service level and minimize scrap. For the evaluation of results, NHA-SCO has been compared to other well-known optimization algorithms. In the presented case studies, the NHA-SCO algorithm performs faster, or it converges in fewer iterations, obtaining similar or even better results than the other algorithms tested.
Learning analytics, dashboard for academic trajectory
(CEUR-WSceurws@sunsite.informatik.rwth-aachen.de, 2019) Peña Ortega, Mario Patricio; Illescas Peña, Lourdes Eugenia; Bravo Guerrero, Fabián Eugenio
© 2019 CEUR-WS. All rights reserved. In the context of university academic management, several proposals have been developed for the study of analysis and visualization of learning trajectories. Bearing in mind that the educational trajectory is the trajectory of the student traveled at a given time from entry to the end of the stay, it can be considered that the use of technology could extract or highlight relevant information that is not seen directly with the tools traditional The visualization of data in educational environments has become a challenge due to the large amounts of information available. The responsibilities of educational administrators require a clear visual proposal adapted to queries based on an academic context. Therefore, it was proposed to generate a tool that generates dashboard based on relevant variables of the students. To do this, the proposal began with a review of the literature that helped analyze the different ways of visualizing the data of academic trajectories. Subsequently, a dynamic visualization was formulated to explain the teachers and authorities through the learning analysis panel, based on the use of parallel coordinates that present multidimensional data over time. The sample was constituted by records of 1975 students of an Ecuadorian university, of the cohort that began in March 2013, distributed by faculties and careers. The technique used allowed us to discover trends and relationships between dimensions, improving the understanding of the trajectory patterns of students, the trends of school dropout, either increase or decrease performance, among other relationships. The consultations allowed to filter data by variables such as: faculties, careers, students and intervals of scores. Finally, the validation of the proposal was made based on the relevance of the dashboard, in response to the inquiries of the academic manager.
Metodological proposal for trajectory analysis. Case study
(Escuela Superior Politécnica del Litoral (ESPOL), Ecuador, Pontificia Universidad Católica de Chile, Chile, 2018) Illescas Peña, Lourdes Eugenia; Peña Ortega, Mario Patricio; Bravo Guerrero, Fabián Eugenio; Larriva González, Silvana Fabiola
For university academic management in general, it is always important to clear-ly know the average time a student needs to complete the credits established in the curriculum. There are several methods to capture academic information and most of them are based on qualification records, access to virtual platforms, as-sistance, etc. Based on the architecture of learning analysis proposed by the au-thors, we specifically worked on a methodology that would allow measuring the academic trajectory using variables such as: number of credits per semester, grades, approvals and number of enrollments. Information from a 2013 cohort of two university careers was used as a reference. The result of the methodolo-gy allowed graphing the behavior and generating a detailed analysis of the aca-demic trajectory. The methodological proposal can be generalized for other ca-reers provided that the analysis of the results is contextualized to the behavior of the nature of the training area.
Causas de deserción en el ingreso a la universidad; un estudio de caso
(2017) Peña Ortega, Mario Patricio; Bravo Guerrero, Fabián Eugenio; Illescas Peña, Lourdes Eugenia; Larriva Gonzalez, Silvana Fabiola
This research analyzes the causes of desertion that are generated in the development of the leveling course prior to the entry into the first year of the faculties of the technical area of University of Cuenca. Studies show that dropout is a global problem that occurs especially in university admission, cause of waste of time and resources to student and institution. It is important to identify main causes of desertion so that decisions can be taken and actions implemented to reduce dropout rates. For this, the student’s socioeconomic records were analyzed; math tests were applied at the beginning and end of the course; a survey at the beginning of the leveling course and another at the end of the course; also, a survey was carried out on a sample of students who dropped out of leveling, and desertion rates were analyzed for students in the technical area. Results suggest some causes of desertion, such as lack of bases in mathematics, not to enter the career of their preference, high academic requirements in technical careers, a complex evaluation system, lack of economic resources, among other reasons for the desertion. It emphasizes the coincidence of the causes of desertion with other studies, and the concern of the institutions to reduce the rates of desertion.
Gráficas de trayectorias escolares de estudiantes de bachillerato
(2021) Illescas Peña, Lourdes Eugenia; Bravo Guerrero, Fabián Eugenio; Peña Ortega, Mario Patricio; Bojorque Pazmiño, Miriam Eliana
For school authorities it is important to know how the students of the institution are progressing in their studies, because in this way they can detect any type of difficulties that arise, however, that information is not always known in time. The objective of this research is to propose a methodology that allows to visualize school trajectories of the students and in this way identify the difficulties that students have in mathematics. To show the potential of the methodology, high school students from an Ecuadorian educational institution were investigated between 2015 and 2019, their grades were processed and graphs of school trajectories were generated, which present the evolution of their grades during the school period. Through this methodology, the identification of individual trajectories of low performance is facilitated, in addition, it was found that collaborative activities have higher scores than individual ones, performance differences between courses were also evidenced. The presentation of grades through graphs is a powerful tool for academic management since it facilitates the detection of difficulties and the making of timely decisions.
Trayectoria académica de estudiantes universitarios
(2020) Bravo Guerrero, Fabián Eugenio; Illescas Peña, Lourdes Eugenia; Peña Ortega, Mario Patricio
Although the careers have a curriculum planning, in practice the student takes some additional time to complete his program. In some careers, this delay can be excessive, implying delays and the loss of resources for the student and the institution. This research was carried out with a cohort that began in March 2013 and ended in July 2018, with the aim of longitudinally analyzing the academic progress of the students, and evidencing the gap between the ideal progress and the real progress in each period, and Thus, to define optimization policies for educational management. A faculty was found where its students took 46.6% more than the ideal term; Additionally, those students, whose parents are professionals or who hold high positions, have academic progress that is statistically superior to others.
Reprobación y abandono de estudiantes universitarios
(2020) Illescas Peña, Lourdes Eugenia; Bravo Guerrero, Fabián Eugenio; Peña Ortega, Mario Patricio
Studies show that in higher education institutions, students experience difficulties during the course of their studies, failing some subjects and in other cases, withdrawing from university. Knowing this reality can lead the authorities to make academic decisions to face this problem and improve terminal efficiency, which is an important indicator for institutional accreditation. The objective of this study was to determine the failure and dropout rates of the students of the different faculties, and at what point in the studies these rates are higher. The research was carried out on 1900 students from the University of Cuenca, Ecuador, a cohort that began their studies in March 2013, and a longitudinal follow-up was carried out until July 2018, using the documentary research technique, for which the academic databases owned by the university. It was found that both repetition and dropout rates are higher in the first semesters of study in all faculties, and that Medical Sciences and Architecture have low rates of loss of subjects and dropout of their students, while some faculties from the Social Sciences area they have high rates of failure and desertion. It is important to know these indicators for each faculty and at all times in the students' trajectory, so that the authorities make decisions that allow improving the academic offer.
A model for implementing enterprise resource planning systems in small and medium-sized enterprises
(Science and Technology Publications, 2021) Tapia Cárdenas, Daniela Estefania; Vintimilla Álvarez, Paola Fernanda; Álvarez Palomeque, Lourdes Ximena; Llivisaca Villazhañay, Juan Carlos; Peña Ortega, Mario Patricio; Guamán Guachichullca, Noé Rodrigo; Sigüenza Guzmán, Lorena Catalina; Jadán Avilés, Diana Carolina; Vintimilla Álvarez, Paola Fernanda
Small and medium-sized enterprises (SMEs) are considered dynamic agents within the business environment. Currently, SMEs have great potential for strong growth and great profit. However, their growth is restricted by the lack of systems that would allow integrating their data and activities. One possible solution is the implementation of Enterprise Resource Planning (ERP) systems to increase the company’s level of efficiency, effectiveness, and productivity. However, implementation processes require investing resources and bring certain problems, e.g., the difficulty to fully adapt to the organization’s accounting and management procedures, and lack of experience of end-users in handling ERP systems. The aim of this study is focused on constructing a model for successfully implementing ERP systems into SMEs. This model used a group of critical success factors (CSF) to analyze empirical evidence in organizations. To its development, the interpretive structural modeling methodology was used, and it was validated in a focus group of experts in implementing and using ERP systems. The results show that the model is adequate for a successful implementation in SMEs engaged in sales, production, or service activities.
Feature engineering based on ANOVA, cluster validity assessment and KNN for fault diagnosis in bearings
(2018) Peña Ortega, Mario Patricio; Cerrada, Mariela; Álvarez Palomeque, Lourdes Ximena; Jadán Avilés, Diana Carolina; Lucero, Pablo M; Barragán Landy, Milton Francisco; Guamán Guachichullca, Noé Rodrigo; Sánchez, René Vinicio
The number of features for fault diagnosis in rotating machinery can be large due to the different available signals containing useful information. From an extensive set of available features, some of them are more adequate than other ones, to classify properly certain fault modes. The classic approach for feature selection aims at ranking the set of original features; nevertheless, in feature selection, it has been recognized that a set of best individually features does not necessarily lead to good classification. This paper proposes a framework for feature engineering to identify the set of features which can yield proper clusters of data. First, the framework uses ANOVA combined with Tukey's test for ranking the significant features individually; next, a further analysis based on inter-cluster and intra-cluster distances is accomplished to rank subsets of significant features previously identified. Our contribution aims at discovering the subset of features that discriminates better the clusters of data associated to several faulty conditions of the mechanical devices, to build more robust multi-fault classifiers. Fault severity classification in rolling bearings is studied to verify the proposed framework, with data collected from a test bed under real conditions of speed and load on the rotating device. © 2018 - IOS Press and the authors. All rights reserved.