Mendoza, Abraham
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Mendoza, Abraham
Official Name
Mendoza Andrade, Abraham
Alternative Name
amendoza
Main Affiliation
ORCID
Scopus Author ID
56002690300
Researcher ID
V-4102-2018

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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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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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Differential Evolution Based Algorithm for Optimal Current Ripple Cancelation in an Unequal Interleaved Power Converter

2021 , Rosas-caro, Julio , Pedro M. García-Vite , Rodríguez Vázquez, Alma Nayeli , Mendoza, Abraham , Alejo-Reyes, Avelina , Erik Cuevas , Francisco Beltran-Carbajal

This paper proposes an optimal methodology based on the Differential Evolution algorithm for obtaining the set of duty cycles of a recently proposed power electronics converter with input current ripple cancelation capability. The converter understudy was recently introduced to the state-of-the-art as the interleaved connection of two unequal converters to achieve low input current ripple. A latter contribution proposed a so-called proportional strategy. The strategy can be described as the equations to relate the duty cycles of the unequal power stages. This article proposes a third switching strategy that provides a lower input current ripple than the proportional strategy. This is made by considering duty cycles independently of each other instead of proportionally. The proposed method uses the Differential Evolution algorithm to determine the optimal switching pattern that allows high quality at the input current side, given the reactive components, the switching frequency, and power levels. The mathematical model of the converter is analyzed, and thus, the decision variables and the optimization problem are well set. The proposed methodology is validated through numerical experimentation, which shows that the proposed method achieves lower input current ripples than the proportional strategy.

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Numerical Optimization of the Capacitors Selection in the MSBA Converter to Reduce the Output Voltage Ripple

2022 , Alejo-Reyes, Avelina , Rodríguez Vázquez, Alma Nayeli , Rosas-caro, Julio , Mendoza, Abraham

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.

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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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An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem

2020 , Alejo-Reyes, Avelina , Erik Cuevas , Rodríguez Vázquez, Alma Nayeli , Mendoza, Abraham , Olivares-Benitez, Elias

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.

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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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Inventory replenishment decisions model for the supplier selection problem facing low perfect rate situations

2019 , Alejo-Reyes, Avelina , Mendoza, Abraham , Olivares-Benitez, Elias

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Inventory replenishment decision model for the supplier selection problem using metaheuristic algorithms

2020 , Alejo-Reyes, Avelina , Olivares-Benitez, Elias , Mendoza, Abraham , Rodríguez Vázquez, Alma Nayeli

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