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 28
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Publication

Digital Twins and Blockchain: Empowering the Supply Chain

2022 , Aguilar Ramírez, José Eduardo , Marmolejo Saucedo, José Antonio , Rodríguez Aguilar, Román

Industry 4.0 is here, and it arrived with very promising new technologies that can foster he supply chain management across industries. In this paper we review multiple sources to identify the main characteristics of Digital Twins and Blockchain technologies and how they can work together to fulfill the needs of the supply chain. We identify some advantages and disadvantages that must be properly analyzed before adopting this approach into any business. Many applications behind these new benefits are still in development, but we believe these two technologies have great potential. © 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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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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Estimation of Electricity Prices in the Mexican Market

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

This paper presents an alpha stable regression model to estimate prices in the Mexican Electric Market. This market began operations in February 2016. The observed prices show great fluctuations in the observed data due to diverse aspects, a seasonality of the demand, the availability of fuel and the problems of congestion in the electrical network. 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 of the 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. © Springer Nature

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Key Factors in the Successful Integration of the Circular Economy Approach in the Industry of Non-durable Goods: A Literature Review

2022 , Jacinto-Cruz, Marcos , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio

Nowadays consumers are more informed about the characteristics of the products they are buying and the services they are using as well as their respective environmental impacts. The nondurable goods industry is the closest to consumers in everyday life, therefore awareness of the environmental impacts of these products has gained greater attention from consumers. In response to increased consumer demand for environmental attributes, the nondurable goods industry has begun to apply circular economy guidelines in its supply chain, in addition to complying with new environmental regulations in various countries. This research addresses a literature review to identify the key factors that allow the correct implementation of the circular economy approach in the non-durable goods industry. Among the main factors identified are the voice of the customer, the traceability and collection of empty containers, as well as and efficient international environmental regulation. © Springer Nature

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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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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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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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Statistical Learning Applied to Malware Detection

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

This work shows an application of statistical learning methodologies in order to determine the important factors for malware detection. Support Vector Machines and Lasso Regression performed Malware classification with additional re-sampling methods. The results show that the Lasso Regression allows an efficient selection of relevant variables for the construction of the classifier, also the integration of support vector machines improves the efficiency of the classifier through the application of resampling methods. The model presented in this paper uses a statistical learning approach through the selection of variables, non-linear classification, and resampling methods. © 2020, 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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