Rodríguez Aguilar, Román
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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 14
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A machine learning-based analytical intelligence system for forecasting demand of new products based on chlorophyll : a hybrid approach

2024 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio , Garcia-Llamas, Eduardo , Rodríguez-Aguilar, Miriam , Marmolejo-Saucedo, Liliana

This manuscript addresses the problem of forecasting the demand for innovative products with limited and inhomogeneous sales data over time. The main objective of the study is to use the information available from a group of innovative chlorophyll-based food products to build a coherent demand forecasting system. From a transactional database, time series were constructed for each group of products, analyzing the stationarity and seasonality of the time series through the Dickey–Fuller and Canova–Hansen tests. Likewise, an ARIMA model, a long short-term memory (LSTM) recurrent deep neural network, and a support vector machine (SVM) were trained to select the best model for each product based on a forecast performance metric. A comparison between classical forecasting techniques and machine learning models is shown. The LSTM neural network was the best model for most products because the internal architecture of the network allows not only to capture non-linear relationships between variables but is also capable of controlling the flow of information to preserve characteristics over time that are relevant for forecasts. The second-best model was the SVM, which allows capturing non-linear behaviors through kernel functions and uses a smaller amount of data for its estimation. Finally, the ARIMA model presented the lowest performance for all products. The objective of having various methodologies is that the system allows the best forecast to be selected according to the type of product, availability of information and methodology used, which will allow the company to integrate new products into the system over time. ©Springer

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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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Designing a resilient supply chain: An approach to reduce drug shortages in epidemic outbreaks

2020 , Lozano-Díez, José Antonio , Marmolejo Saucedo, José Antonio , Rodríguez Aguilar, Román

Introduction: Supply network design is a long-studied topic that has evolved to address disruptive situations. The risk of supply chain disruption leads to the development of resilient supply chains that are capable of reacting effectively. Objectives: In the context of public health, drug supply networks face shortage challenges in many situations, such as current epidemic outbreaks such as COVID-19. Drug shortages can occur due to manufacturing problems, lack of infrastructure, and immediate reaction mechanisms. Methods: The case study is solved with anyLogistix optimization and simulation software. RESULTS: We present the results of a hypothetical study on the impact of COVID-19 on a regional supply network. The results of this research are intended to be the basis for the design of resilient supply chains in epidemic outbreaks. Conclusión: Drug providers should consider strategies to prevent or reduce the impact of shortages as well as disruption spreads. ©2020 José Antonio Lozano Díez, José Antonio Marmolejo Saucedo & Roman Rodríguez Aguilar, licensed to European Alliance for Innovation.

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Out of pocket and catastrophic health spending in Mexico in the face of the COVID-19 pandemic

2023 , Rodríguez Aguilar, Román , Zavala Landin, Alejandro , Marmolejo Saucedo, José Antonio , Rodriguez Aguilar, Miriam , Marmolejo Saucedo, Liliana

Introduction: The measurement of the financial coverage of a health system uses key indicators such as household out-of-pocket spending as well as catastrophic health spending. Said indicators depend on the financing structure of the health system as well as quality criteria and efficiency of the system in patient care. In the case of Mexico, in recent years there have been important changes in the structure of the health system in addition to suffering from the COVID-19 pandemic events that have significantly impacted the access to health of patients. Therefore, it is relevant to quantify the impact of these events on out-of-pocket spending and catastrophic spending on health in Mexico and have a robust diagnosis of the financial coverage of the system public health in Mexico.OBJECTIVES: The main objective of this study is to quantify out-of-pocket spending and catastrophic spending on health in Mexican households for the year 2020. Comparing these estimates with previous years given the recent changes in the Mexican health system as well as the effect of the COVID-19 pandemic in these indicators. Methods: Based on the information available in the 2020 National Household Income and Expenditure Survey (ENIGH), out-of-pocket and catastrophic spending on health were estimated following the methodology proposed by the World Health Organization. A quantile regression was estimated to assess the effect of income distribution on out-of-pocket spending. Conclusion: The effect of recent modifications to the public health system in Mexico in addition to the COVID-19 pandemic has been reflected in an increase in the percentage of households with out-of-pocket spending in Mexico, as well as the percentage of households with catastrophic spending in health. The main expense item is made in medicines, ambulatory care follow-up and hospitalization. It is a priority to establish efficient financial protection schemes that allow reversing this situation in terms of efficient access to health in Mexico.

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Selecting the Distribution System using AHP and Fuzzy AHP Methods

2024 , Saucedo-Martínez, Jania Astrid , Salais-Fierro, Tomás Eloy , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio

In this research, we present a supporting tool for decision making by designing a distribution system for a trading company of supplies for the welding industry in Mexico. The case study encompasses a distribution system with shortage problems and poor fleet capacity. To address these problems, improvement options were grouped into three possible scenarios through a third-party logistics (3PL) service. Furthermore, for the evaluation and selection of one of the scenarios, the Analytic Hierarchy Process (AHP) methodology was proposed integrating fuzzy logic as a tool for decision making, including factors of uncertainty and subjectivity as well as a comparison with traditional AHP obtaining the best scenario, meeting the requirements of the company, and showing potential improvements in the desired service level for its distribution system. © 2024 Springer Nature

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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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Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle

2019 , Vasant, Pandian , Marmolejo Saucedo, José Antonio , Litvinchev, Igor , Rodríguez Aguilar, Román

Currently, there is a remarkable focus on green technologies for taking steps towards more use of renewable energy sources within the sector of transportation and also decreasing pollution. At this point, employment of plug-in hybrid electric vehicles (PHEVs) needs sufficient charging allocation strategy, by running smart charging infrastructures and smart grid systems. In order to daily usage of PHEVs, daytime charging stations are required and at this point, only an appropriate charging control and a management of the infrastructure can lead to wider employment of PHEVs. In this study, four swarm intelligence based optimization techniques: particle swarm optimization (PSO), gravitational search algorithm (GSA), accelerated particle swarm optimization, and hybrid version of PSO and GSA (PSOGSA) have been applied for the state-of-charge optimization of PHEVs. In this research, hybrid PSOGSA has performed very well in producing better results than other stand-alone optimization techniques. © 2021 Springer Nature Switzerland AG. Part of Springer Nature.

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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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Quantum-behaved bat algorithm for solving the economic load dispatch problem considering a valve-point effect

2020 , Vasant, Pandian , Parvez Mahdi, Fahad , Marmolejo Saucedo, José Antonio , Litvinchev, Igor , Rodríguez Aguilar, Román , Watada, Junzo

Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in order to minimize total fuel cost of the system, while satisfying all other constraints. To make the system more applicable to the real-world problem, a valve-point effect is considered here with the ELD problem. QBA is applied in 3-unit, 10-unit, and 40-unit power generation systems for different load demands. The obtained result is then presented and compared with some well-known methods from the literature such as different versions of evolutionary programming (EP) and particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA) and hybrid ABC_PSO. The comparison of results shows that QBA performs better than the above-mentioned methods in terms of solution quality, convergence characteristics and computational efficiency. Thus, QBA proves to be an effective and a robust technique to solve such nonlinear optimization problem.

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Machine Learning for Digital Shadow Design in Health Insurance Sector

2024 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio , Rodríguez-Aguilar, Miriam , Marmolejo-Saucedo, Liliana

The digital transformation process in organizations has accelerated significantly in recent years; the COVID-19 pandemic was a catalyst that highlighted the need for digitalization in all sectors. In the case of the health sector, this process is complex due to the processes inherent in health care as well as the integration of multiple sectors that allow the provision of health services. A first approach towards the construction of a Digital Twin in health organizations is a Digital Shadow that allows an orderly transition towards digital operation in real time. This paper presents a first approach to the design of a Digital Shadow for the health insurance sector and specifically for the care of patients diagnosed with COVID-19 through the implementation of an analytical intelligence system based on machine learning models to forecast and monitor to patients who represent catastrophic cases for the insurer. © 2024 Springer Nature

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