An Improved GreyWolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem
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Fecha
2020-08
Autores
Alejo-Reyes, Avelina
Cuevas, Erik
Mendoza, Abraham
Olivares-Benitez, Elias
Rodríguez-Vázquez, Alma N.
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Editor
MDPI
Resumen
Descripción
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
di erent 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 di cult 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 di cult 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.
Palabras clave
Metaheuristic Algorithms, Grey Wolf Optimizer, Supply Chain Management
Citación
Alejo-Reyes, A.; Cuevas, E.; Rodríguez, A.; Mendoza, A.; Olivares-Benitez, E. An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem. Mathematics 2020, 8, 1457.