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

1 Facultad de Ingenieria, Universidad Panamericana, Alvaro del Portillo 49, Zapopan 45010, Mexico
2 Centro Universitario de Ciencias Exactas e Ingenieria, Universidad de Guadalajara, Blvd. Marcelino Garcia Barragan 1421, Guadalajara 44430, Mexico

Special Issues: Bio-inspired algorithms and Bio-systems

In supply chain management, fast and accurate decisions in supplier selection and order quantity allocation have a strong influence on the company's profitability and the total cost of finished products. In this paper, a novel and non-linear model is proposed for solving the supplier selection and order quantity allocation problem. The model is introduced for minimizing the total cost per time unit, considering ordering, purchasing, inventory, and transportation cost with freight rate discounts. Perfect rate and capacity constraints are also considered in the model. Since metaheuristic algorithms have been successfully applied in supplier selection, and due to the non-linearity of the proposed model, particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), are implemented as optimizing solvers instead of analytical methods. The model is tested by solving a reference model using PSO, GA, and DE. The performance is evaluated by comparing the solution to the problem against other solutions reported in the literature. Experimental results prove the effectiveness of the proposed model, and demonstrate that metaheuristic algorithms can find lower-cost solutions in less time than analytical methods.
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Keywords metaheuristic algorithms; particle swarm optimization; genetic algorithm; differential evolution; inventory management; supply chain management; supplier selection; order quantity allocation

Citation: Avelina Alejo-Reyes, Elias Olivares-Benitez, Abraham Mendoza, Alma Rodriguez. Inventory replenishment decision model for the supplier selection problem using metaheuristic algorithms. Mathematical Biosciences and Engineering, 2020, 17(3): 2016-2036. doi: 10.3934/mbe.2020107


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