Rodríguez Aguilar, Román
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Preferred name
Rodríguez Aguilar, Román
Official Name
Rodríguez Aguilar, Román
Alternative Name
rrodrigueza
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ORCID
Scopus Author ID
56717663000
Researcher ID
AAO-6158-2020

Research Output
Now showing 1 - 10 of 31
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Prices of Mexican Wholesale Electricity Market: An Application of Alpha-Stable Regression

2019 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio , Brenda Retana-Blanco

This paper presents a proposal to estimate prices in the Mexican Wholesale Electric Market, which began operations in February 2016, which is why it moves from a scheme with a single bidder to a competitive market. There are particularities in the case of the Mexican market, the main one being the gradual increase in the number of competitors observed until now and, on the other hand, the geographic and technical characteristics of the electric power generation. The observed prices to date show great fluctuations in the observed data due to diverse aspects; among the stems we can mention the own seasonality of the demand of electrical energy, the availability of fuel, the problems of congestion in the electrical network, as well as other risks such as natural hazards. For the above, it is relevant in a market context to have a price estimation as accurate as possible for the decision-making of supply and demand. This paper proposes a methodology for the generation of electricity price estimation through the application of stable alpha regressions, since the behavior of the electric market has shown the presence of heavy tails in its price distribution. © 2019 by the authors, Sustainability.

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Machine Learning Applied to the Measurement of Quality in Health Services in Mexico: The Case of the Social Protection in Health System

2018 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio , Vasant, Pandian

To propose a satisfaction indicator of users of health services affiliated to the Social Protection System in Health (SPSS). Identify the effect of the main factors that are directly related to the satisfaction level and perception of quality of health services. A machine-learning model based on Logistic Models and Principal Components was developed to estimate a satisfaction index. The survey data collected for the “SPSS 2014 User’s Satisfaction Study” was used, considering a sample of 28,290 users. The proposed model shows, in general, the positive perception of quality of health services (national average 0.0756). There are factors statistically significant that influence these results, the good perception of infrastructure (OR:2.12; CI 95%:1.9–2.36); the gratuity of the service provided (OR:1.98; CI 95%: 1.42–2.76); and full medicines supply (OR:1.81; CI 95%:1.91–2.36). The proposed index can be used as an indicator for improving health care quality of the population covered by the SPSS. © 2019, Springer Nature Switzerland AG.

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Structural Dynamics and disruption events in Supply Chains using Fat Tail Distributions

2019 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio

The analysis of structural dynamics in a supply chain requires robust methods for the modeling of disruption events that can be faced. Statistical modeling, the machine learning application and access to large amounts of data require much more realistic models to manage risk in the supply chain. This study proposes a statistical methodology for the modeling of disruption events in the supply chain with heavy tailed distributions, which will allow the construction of models more closely linked to reality for risk management in the supply chain. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

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The supply chain event management application: a case study

2019 , Palma, Fanny , Saucedo, Jania A. , Marmolejo Saucedo, José Antonio , Rodríguez Aguilar, Román

A deviation from planned processes reveals the consequences that a disruption can cause in any area, even more a disruption with Ripple Effect (RE), which implied for the global Supply Chain (SC) when depends essentially from the stakeholders in all levels and chains. The learnt lessons from these tragical events show that many companies could not assess the impact and its side effects, therefore they cannot respond adequately, prolonging the crisis and expanding the disruption. A disruption evaluation model would allow to know from the beginning the potential impact to check carefully into the critical events to provide the necessary resources to control it. Hence, it is relevant that SC managers know and employ the right tools to focus on the control and the solution; as well to evaluate the impact and the disruption criticality level to mitigate and control the impact with total certainty that the appropriate actions are being taken according to the problematic. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

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Optimization Methods, Mobile Networks and Data Analytics: Applications in Engineering and Industry 4.0 : Editorial

2020 , Marmolejo Saucedo, José Antonio , Martínez Ríos, Félix Orlando , Rodríguez Aguilar, Román

The business world is changing and demands the integration of various engineering techniques to make the operation of the system in general more efficient. Optimization, information security and a prospective business vision is essential for companies to be more productive. The application of new technological solutions to manufacturing and management processes is one of the ways through which digital transformation in the supply chain advances, on the road to industry 4.0. To achieve this, optimization techniques, soft-computing, machine learning and Big Data technologies must be combined. With this integration it is possible to work with real-time sensed data to build and simulate Digital Twins with very high precision. © Springer Nature

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Conceptual framework of Digital Health Public Emergency System: digital twins and multiparadigm simulation

2020 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio

Introduction: Two major technological paradigms have been developed in recent years, digital twins and the multiparadigm simulation. In the Health Sector, the enormous potential of both approaches for the management of public health emergencies is envisioned. Objectives: This study aims to develop the conceptual framework for the development of a Digital Public Health Emergency System. Methods: The integration of the digital twins in health with the multi-paradigm simulation for the design of a digital system of public health emergencies is proposed.

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Improving a Manufacturing Process using Recursive Artificial Intelligence

2021 , Marmolejo Saucedo, José Antonio , Rodríguez Aguilar, Román , Romero Perea, Uriel Abel , Garrido Vaqueiro, Manuel , Robredo Hernández, Regina , Sánchez Ramírez , Fernando , Martínez , Ana Paula

This work explores the improvements that can be made in the process of parametrization of discrete-event simulation models. A manufacturing process is modeled through queuing systems and alternative decisions to perform production, transport, and merchandise handling tasks. The use of recursive artificial intelligence is suggested to improve the quality of the parameters used in the simulation model. Specifically, a vector support machine is used for statistical learning. A relevant characteristic of the proposed model is the integration of different information technology platforms so that the simulation can be recursive. ©2021, IFIP International Federation for Information Processing.

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A proposal for the supply chain design : a digitization approach

2018 , Marmolejo Saucedo, José Antonio , Retana-Blanco, Brenda , Pedraza-Arroyo, Erika , Rodríguez Aguilar, Román

The logistics network of an automotive company in Mexico, was analyzed to propose a better logistics network in the country to improve delivery times to customers. The network design considers elements of digitization of Greenfield Analysis and Network Optimization processes. Taking into account the information given by the company, it was possible to obtain optimal scenarios for better operation, which involved the construction of distribution centers throughout Mexico. © 2020 Jose Antonio Marmolejo-Saucedo et al., licensed to EAI.

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Estimation of the Stochastic Volatility of Oil Prices of the Mexican Basket: An Application of Boosting Monte Carlo Markov Chain Estimation

2021 , Marmolejo Saucedo, José Antonio , Rodríguez Aguilar, Román

The volatility of the returns on financial assets is not a constant number over time as many valuation models, mainly derivatives, developed during the 80's, assume. The complexity of non-heteroscedasticity and the difference in results when estimated with different methodologies such as historical, implicit or stochastic calculation, make this subject too extensive a field to be covered in this work. However, stochastic volatility has been widely accepted in recent years. Monte Carlo Markov Chain (MCMC) method is explained and used to estimate the distribution of oil prices of Mexican basket as a stochastic variable. MCMC in the univariate case, supposes that we can estimate the distribution of a latent (hidden) variable through the behavior of another variable observed posteriori with the help of Bayesian inference; this method allows an efficient inference independent of the underlying process through an algorithm. The results show a correct adjustment of stochastic volatility to the behavior of the oil prices. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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A Hybrid Model for Improving the Performance of Basketball Lineups

2020 , Rodríguez Aguilar, Román , Infante-Escudero, Rodrigo , Marmolejo Saucedo, José Antonio

An optimization model of the NBA team lineups is presented to improve the performance of the teams according to the selected lineup. A set of variables such as inputs and outcome variables are taken into account to optimize the results. Additionally, a technical efficiency analysis was performed on the performance of the lineups selected by the optimization model to validate the results. The results show that the lineups selected through the optimization model were those with greater technical efficiency for the equipment. The application of optimization methods and technical efficiency can be a robust tool for decision making in the sports field. © Springer Nature Switzerland AG 2020.

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