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

Research Output

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Multi-period multi-product closed loop supply chain network design: A relaxation approach

2021 , Subramanian Pazhani , Mendoza, Abraham , Ramkumar Nambirajan , T.T. Narendran , K. Ganesh , Olivares-Benitez, Elias

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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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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 Memetic Algorithm for the Cumulative Capacitated Vehicle Routing Problem Including Priority Indexes

2020 , Nucamendi-Guillén, Samuel , Flores Díaz, Diego , Mendoza, Abraham , Olivares-Benitez, Elias

This paper studies the Cumulative Capacitated Vehicle Routing Problem, including Priority Indexes, a variant of the classical Capacitated Vehicle Routing Problem, which serves the customers according to a certain level of preference. This problem can be effectively implemented in commercial and public environments where customer service is essential, for instance, in the delivery of humanitarian aid or in waste collection systems. For this problem, we aim to minimize two objectives simultaneously, the total latency and the total tardiness of the system. A Mixed Integer formulation is developed and solved using the AUGMECON2 approach to obtain true efficient Pareto fronts. However, as expected, the use of commercial software was able to solve only small instances, up to 15 customers. Therefore, two versions of a Memetic Algorithm with Random Keys (MA-RK) were developed to solve the problem. The computational results show that both algorithms provided good solutions, although the second version obtained denser and higher quality Pareto fronts. Later, both algorithms were used to solve larger instances (20–100 customers). The results were mixed in terms of quality but, in general, the MA-RK v2 consistently outperforms the first version. The models and algorithms proposed in this research provide useful insights for the decision-making process and can be applied to solve a wide variety of business situations where economic, customer service, environmental, and social concerns are involved.

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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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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 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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