Alejo-Reyes, Avelina

Main Affiliation

Facultad de Ingeniería - CampGDL

Preferred name

ORCID

0000-0001-9903-7476

Researcher ID

EKT-0166-2022

Scopus Author ID

57202425879

Now showing 1 - 10 of 37

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Energy Recycling Laboratory Experimental Test Bench for Three-Phase FACTS Devices Prototypes
(2019)
Jesus E. Valdez-Resendiz
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Mayo Maldonado, Jonathan
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Rosas-caro, Julio
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Alejo-Reyes, Avelina
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Armando Llamas-Terres
Scopus© Citations 1 2 17
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Algorithms for Supplier Selection and Order Quantity Allocation
(2020)
Alejo-Reyes, Avelina
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Mendoza Andrade, Abraham
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Olivares Benitez, Elías
Supply chain management is particularly important because of its influence on a compan\¶s profitabilit\ and competitiYeness. Among the different actiYities inYolYed in supply chain management, purchasing decisions, supplier selection, and order quantity allocation have a direct impact on the cost of the produced items. The cost function usually deals with non-linear equation systems with an infinite number of possible solutions. The result is an optimal inventory policy with a minimum cost per time unit. This research addresses the supplier selection and order quantity allocation problem. The objective is to allocate the corresponding order quantities over time to the selected suppliers, while minimizing inventory and transportation costs, simultaneously. In selecting suppliers, two feasibility constraints are considered: capacity and quality (perfect rate). Typically, in the literature, the acceptable perfect quality rate of raw materials is ensured with a mathematical inequality in the model constraints. Therefore, this research first addresses the desired perfect rate by including it as part of the order cycle parameters calculation and not as an individual constraint. The main advantages of doing so are: (i) it leads to lower-cost solutions compared to previously proposed literature, (ii) it effectively faces the so-called low perfect rate situations, by providing feasible solutions when the perfect rate of suppliers is smaller than the minimum perfect-rate required by the customer. A sensitivity analysis was carried out on the proposed model to analyze the effect of some parameters on the total cost per time unit. Results showed that transportation costs have an important effect on the order quantity and that the price levels do not necessarily affect the number of purchased units. Hence the importance of considering transportation costs when making order quantity allocation decisions. Another challenge of the problem under study is that the model is non-linear and has an infinite number of possible solutions because of the continuous nature of the variables. Therefore, there is a need from the scientific and industry communities to find solutions in an efficient and timely manner. Former studies introduced limits to the length of the order cycle or to the number of orders in the order cycle in order to obtain a solution using commercial software. However, computers still take many hours or days to provide optimal solutions, if at all. Therefore, second, this research applies different metaheuristic algorithms to solve the problem, namely: particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE). With these algorithms, a larger solution space can be explored while getting a solution in the order of seconds; this allows cheaper solutions to be found. PSO, GA, and DE are well known metaheuristic algorithms in the optimization field and have been used to solve lot-sizing, and supplier selection problems. New metaheuristic methods are commonly proposed for particular circumstances, for example, converging to an optimal solution faster than other strategies. A recently proposed metaheuristic algorithm, the Grey Wolf Optimizer (GWO), was explored in this research. The algorithm was modified and adapted to the supplier selection and order quantity allocation problem when the amount of decision variables is too large. The improved GWO method, called iGWO, includes weighted factors and a displacement vector to promote the exploration of the search strategy avoiding the use of unfeasible solutions. The iGWO was tested and results showed that, in addition to obtain optimal solutions, it performed a better search strategy, finding feasible solutions in all instances of the tested problem. Finally, based on the knowledge acquired through the previous contributions, a heuristic algorithm to solve the problem under study is proposed. This heuristic algorithm allows to extend the explored solution space to an exceptionally large limit. The solutions obtained with the proposed heuristic algorithm were compared against the solutions obtained with PSO and DE. Two numerical examples are solved. In the first one, it is shown that the proposed heuristic performed best compared to other solutions previously published in the literature, both in terms of computational time and total cost. In the second numerical example, larger instances were studied. Our findings show that the proposed heuristic was able to find a feasible solution, while PSO and DE were unable to find a solution. Therefore, the proposed heuristic does not just lead to lower total cost solutions, but it also performs a more exhaustive search in shorter computational times for larger instances of the problem.
17 36
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Data-driven modeling of proton-exchange membrane fuel cell stacks
(Elsevier BV, 2025-01)
Edgar Silva-Vera
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Jesus E. Valdez-Resendiz
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Alejo-Reyes, Avelina
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Jesse Y. Rumbo-Morales
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Rosas-Caro, Julio
25
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An Evolutionary Algorithm-Based PWM Strategy for a Hybrid Power Converter
(2020)
Alejo-Reyes, Avelina
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Erik Cuevas
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Francisco Beltran-Carbajal
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Rosas-caro, Julio
<jats:p>In the past years, the interest in direct current to direct current converters has increased because of their application in renewable energy systems. Consequently, the research community is working on improving its efficiency in providing the required voltage to electronic devices with the lowest input current ripple. Recently, a hybrid converter which combines the boost and the Cuk converter in an interleaved manner has been introduced. The converter has the advantage of providing a relatively low input current ripple by a former strategy. However, it has been proposed to operate with dependent duty cycles, limiting its capacity to further decrease the input current ripple. Independent duty cycles can significantly reduce the input current ripple if the same voltage gain is achieved by an appropriate duty cycle combination. Nevertheless, finding the optimal duty cycle combination is not an easy task. Therefore, this article proposes a new pulse-width-modulation strategy for the hybrid interleaved boost-Cuk converter. The strategy includes the development of a novel mathematical model to describe the relationship between independent duty cycles and the input current ripple. The model is introduced to minimize the input current ripple by finding the optimal duty cycle combination using the differential evolution algorithm. It is shown that the proposed method further reduces the input current ripple for an operating range. Compared to the former strategy, the proposed method provides a more balanced power-sharing among converters.</jats:p>
Scopus© Citations 3 1 10
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Double-Dual DC-DC Conversion: A Survey of Contributions, Generalization, and Systematic Generation of New Topologies
(2023)
Jesus E. Valdez-Resendiz
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Alejo-Reyes, Avelina
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Rosas-caro, Julio
Scopus© Citations 8 2 15
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Inventory replenishment decision model for the supplier selection problem using metaheuristic algorithms
(2020)
Alejo-Reyes, Avelina
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Olivares-Benitez, Elias
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Mendoza, Abraham
Scopus© Citations 17 1 16
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Numerical Optimization of the Capacitors Selection in the MSBA Converter to Reduce the Output Voltage Ripple
(2022)
Alejo-Reyes, Avelina
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Rosas-caro, Julio
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Mendoza, Abraham
<jats:p>DC–DC power electronics converters are widely used in many applications, such as renewable energy systems. The multistage-stacked boost architecture (MSBA) converter is a large voltage gain converter whose PWM scheme may reduce a percentage of the output voltage ripple, taking advantage of the symmetry of the voltage signals in capacitors (they are triangular waveforms) to have a symmetry cancelation. The switching ripple is unavoidable; the correct selection of components can reduce it, but this may result in a large amount of stored energy (larger size). The selection of capacitors influences the output voltage ripple magnitude. This article proposes a design methodology that combines a recently introduced PWM scheme with a numerical optimization method to choose the capacitors for the MSBA converter. The objective is to minimize the output voltage ripple by choosing two capacitors simultaneously while ensuring the constraint of a certain (maximum) amount of stored energy in capacitors is not overpassed. The internal optimization was performed with the differential evolution algorithm. The results demonstrate that the proposed method that includes numerical optimization allows having a very low output voltage ripple with the same stored energy in capacitors compared to the traditional converter. In a design exercise, up to 60% reduction was observed in the output voltage ripple with the same stored energy in capacitors.</jats:p>
2 13Scopus© Citations 3
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Corner Detection Algorithm Based on Cellular Neural Networks (CNN) and Differential Evolution (DE)
(2021)
Erik Cuevas
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Alejo-Reyes, Avelina
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Del-Valle-Soto, Carolina
Scopus© Citations 3 1 13
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Power quality improvement by interleaving unequal switching converters
(2016)
Arias-Angulo Juan Pedro
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Rosas-caro, Julio
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Beltran-Carbajal Francisco
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Valderrabano-Gonzalez, Antonio
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Haro-Sandoval, Eduardo
1 15Scopus© Citations 7
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Optimal evaluation of re-opening policies for COVID-19 through the use of metaheuristic schemes
(2023)
Erik Cuevas
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Marco Perez
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Jesús Murillo-Olmos
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Bernardo Morales-Castañeda
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Ram Sarkar
Scopus© Citations 7 1 6

Alejo-Reyes, Avelina

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