Alejo-Reyes, Avelina

Main Affiliation
Preferred name
Alejo-Reyes, Avelina
ORCID
0000-0001-9903-7476
Researcher ID
EKT-0166-2022
Scopus Author ID
57202425879

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    Algorithms for Supplier Selection and Order Quantity Allocation
    (2020)
    Alejo-Reyes, Avelina  
    ;
    Mendoza Andrade, Abraham
    ;
    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.
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    Optimization in Industrial Engineering
    (Springer Nature Switzerland, 2025)
    Cuevas, Erik
    ;
    Rosas-Caro, Julio  
    ;
    Alejo-Reyes, Avelina  
    ;
    González Ayala, Paulina
    ;
    Rodriguez, Alma
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    Power quality improvement by interleaving unequal switching converters
    (2016)
    Arias-Angulo Juan Pedro
    ;
    Rosas-caro, Julio  
    ;
    Beltran-Carbajal Francisco
    ;
    Valderrabano-Gonzalez, Antonio  
    ;
    Haro-Sandoval, Eduardo
    Scopus© Citations 7  41  1
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    Statistical Analysis of Lean Construction Barriers to Optimize Its Implementation Using PLS-SEM and PCA
    (2024)
    Romo Gamboa, Rubén  
    ;
    Alejo-Reyes, Avelina  
    ;
    Orozco, Francisco  
    <jats:p>The construction industry performs many tasks scheduled and related to other activities. Companies must optimize their operations, increase efficiency, eliminate waste, and deliver better products to their customers. As a result, this study aims to identify the main challenges associated with the implementation of the Lean Construction model in small and medium-sized construction companies and optimize the implementation of this process using statistically-focused mathematical models. This study was conducted using the partial least squares (PLS-SEM) method and also carried out the principal component analysis to optimize Lean barriers so that they can be properly implemented in the construction industry. The most important obstacles are displayed, as well as the relationships with other factors. Significant relationships have been discovered between the barriers to Lean construction adoption, especially with regard to corporate culture, communication, training, leadership, and the influence of mentality on business and employee adaptability. Construction executives and managers can make well-informed policy and strategic decisions by having a thorough understanding of the main barriers to Lean implementation. This information enables them to focus on the implementation of Lean technologies in projects, to increase market competitiveness, reduce waste and enhance overall work efficiency.</jats:p>
      64
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    Numerical Optimization of Switching Ripples in the Double Dual Boost Converter through the Evolutionary Algorithm L-SHADE
    (2020)
    Alejo-Reyes, Avelina  
    ;
    Erik Cuevas
    ;
    Robles-Campos, Héctor R.  
    ;
    Rosas-caro, Julio  
    <jats:p>Power-electronics based converters are essential circuits in renewable energy applications such as electricity generated with photovoltaic panels. The research on the field is getting increasing attention due to climate change problems and their possible attenuation with the use of renewable energy. Mathematical models of the converters are being used to optimize several aspects of their operation. This article is dedicated to optimizing (through the mathematical model and an evolutionary algorithm) the operation of a state-of-the-art converter. The converter, which is composed of two parts or phases, is controlled by pulse width modulation with two switching signals (one for each phase). The converter provides by itself low switching ripple in both the output voltage and the input current, which is beneficial for renewable energy applications. In the traditional operation, one of the switching signals has an algebraic dependence on the other one. This article proposes a new way to select the duty cycle for both signals. In the proposed method, duty cycles of both phases are considered independent of each other; this provides an extra degree of freedom; on the other hand, this produce that the possible combinations of duty cycles which produce a certain voltage gain is infinite, it becomes a problem with infinite possible solutions. The proposed method utilizes the a linear success-history based adaptive differential evolution with linear population reduction, also called L-SHADE algorithm for simplicity, to find the two duty cycles that achieve the desired voltage gain and to minimize the converters switching ripple. The obtained results are compared with the former operation of the converter; the proposed operation achieves a lower output voltage ripple while achieving the desired operation (voltage gain).</jats:p>
    Scopus© Citations 5  12  1
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    Scopus© Citations 3  18  1
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    An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem
    (2020)
    Alejo-Reyes, Avelina  
    ;
    Erik Cuevas
    ;
    Mendoza, Abraham  
    ;
    Olivares-Benitez, Elias  
    <jats:p>Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. 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. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.</jats:p>
    Scopus© Citations 9  9  1
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    Data-driven modeling of proton-exchange membrane fuel cell stacks
    (2025)
    Edgar Silva-Vera
    ;
    Jesus E. Valdez-Resendiz
    ;
    Alejo-Reyes, Avelina  
    ;
    Jesse Y. Rumbo-Morales
    ;
    Julio C. Rosas-Caro
      8