Alejo-Reyes, Avelina
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Alejo-Reyes, Avelina
Alternative Name
aalejo
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ORCID
Scopus Author ID
57202425879
Researcher ID
EKT-0166-2022

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A heuristic method for the supplier selection and order quantity allocation problem

2021 , Alejo-Reyes, Avelina , Mendoza, Abraham , Olivares-Benitez, Elias

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Blood Vessel Segmentation Using Differential Evolution Algorithm

2021 , Erik Cuevas , Rodríguez Vázquez, Alma Nayeli , Alejo-Reyes, Avelina , Del-Valle-Soto, Carolina

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Inventory replenishment decisions for the supplier selection problem considering transportation freight rates and quality

2018 , Alejo-Reyes, Avelina , Mendoza, Abraham , Olivares-Benitez, Elias

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Quadratic buck–boost converter with positive output voltage and minimum ripple point design

2018 , Rosas-caro, Julio , Jesus E. Valdez‐Resendiz , Mayo Maldonado, Jonathan , Alejo-Reyes, Avelina , Valderrabano-Gonzalez, Antonio

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A Modified Simulated Annealing (MSA) Algorithm to Solve the Supplier Selection and Order Quantity Allocation Problem with Non-Linear Freight Rates

2023 , González Cabrera, Nora Paulina , Alejo-Reyes, Avelina , Erik Cuevas , Mendoza, Abraham

Economic Order Quantity (EOQ) is an important optimization problem for inventory management with an impact on various industries; however, their mathematical models may be complex with non-convex, non-linear, and non-differentiable objective functions. Metaheuristic algorithms have emerged as powerful tools for solving complex optimization problems (including EOQ). They are iterative search techniques that can efficiently explore large solution spaces and obtain near-optimal solutions. Simulated Annealing (SA) is a widely used metaheuristic method able to avoid local suboptimal solutions. The traditional SA algorithm is based on a single agent, which may result in a low convergence rate for complex problems. This article proposes a modified multiple-agent (population-based) adaptive SA algorithm; the adaptive algorithm imposes a slight attraction of all agents to the current best solution. As a proof of concept, the proposed algorithm was tested on a particular EOQ problem (recently studied in the literature and interesting by itself) in which the objective function is non-linear, non-convex, and non-differentiable. With these new mechanisms, the algorithm allows for the exploration of different regions of the solution space and determines the global optimum in a faster manner. The analysis showed that the proposed algorithm performed well in finding good solutions in a reasonably short amount of time.

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A Mathematical Model for an Inventory Management and Order Quantity Allocation Problem with Nonlinear Quantity Discounts and Nonlinear Price-Dependent Demand

2023 , Alejo-Reyes, Avelina , Mendoza, Abraham , Erik Cuevas , Alcaraz Rivera, Miguel

This article focuses on solving the order quantity allocation problem for retailers. It considers factors such as quality constraints, nonlinear quantity discounts, and price-dependent demand. By formulating it as a nonlinear maximization problem, the article aims to find the best combination of suppliers and order quantity out of infinite solutions to maximize the retailer’s profit. The main contribution of this research is a new mathematical model that can solve the problem of quality constraint and demand in a single step. This problem is complex due to the number of equations, their nonlinear nature, and the various trade-offs given by the market. Additionally, this research considers demand as output and includes price-dependent demand, which is more realistic for retailers. The proposed model was tested using an example from the recent literature and showed better results than the previously published best solution regarding profit maximization.

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Optimal evaluation of re-opening policies for COVID-19 through the use of metaheuristic schemes

2023 , Erik Cuevas , Rodríguez Vázquez, Alma Nayeli , Marco Perez , Jesús Murillo-Olmos , Bernardo Morales-Castañeda , Ram Sarkar , Alejo-Reyes, Avelina

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Optimal Selection of Capacitors for a Low Energy Storage Quadratic Boost Converter (LES-QBC)

2023 , Jose Solis-Rodriguez , Rosas-caro, Julio , Alejo-Reyes, Avelina , Jesus E. Valdez-Resendiz

This article studies a recently proposed dc-dc converter and its optimization in terms of capacitors selection through the Particle Swarm Optimization (PSO) algorithm. The converter under study is the so-called Low Energy Storage Quadratic Boost Converter (LES-QBC), a quadratic type of converter that offers a smaller Output Voltage Ripple (OVR) compared to the traditional quadratic boost topology with capacitors of the same characteristics. This study presents a way to select the capacitors for minimizing the OVR while achieving a constraint of a maximum stored energy in capacitors. The capacitor’s stored energy is given as a design specification. The results are compared against the traditional quadratic boost converter and the LES-QBC without optimization (equal capacitance in capacitors). The optimization algorithm used was the so-called Particle Swarm Optimization (PSO). The experimental results demonstrate the effectiveness of the proposition. For the design exercise used for the results, the capacitor’s stored energy was kept almost the same, and a reduction in the OVR was achieved versus the non-optimized LES-QBC.

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Energy Recycling Laboratory Experimental Test Bench for Three-Phase FACTS Devices Prototypes

2019 , Jesus E. Valdez-Resendiz , Mayo Maldonado, Jonathan , Rosas-caro, Julio , Alejo-Reyes, Avelina , Armando Llamas-Terres , Valderrabano-Gonzalez, Antonio , Del-Valle-Soto, Carolina , Valdivia, Leonardo

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Metaheuristic Algorithms Applied to the Inventory Problem

2021 , Erik Cuevas , Rodríguez Vázquez, Alma Nayeli , Alejo-Reyes, Avelina , Del-Valle-Soto, Carolina

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