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An Efficient Algorithm Applied to Optimized Billing Sequencing
Un algoritmo eficiente aplicado a la secuencia de facturación optimizada
DOI:
https://doi.org/10.15446/ing.investig.v42n2.83394Keywords:
Iterative Greedy Algorithm, Genetic Algorithm, Maximize Billing, Distribution Center. (en)Algoritmo Guloso Iterativo, Algoritmo Genético, Maximizar Facturación, Centro de Distribución. (es)
This paper addresses the Optimized Billing Sequencing (OBS) problem to maximize billing of the order portfolio of a typical Distribution Center (DC). This is a new problem in the literature, and the search for the best billing mix has generated demands for better optimization methods for DCs. Therefore, the objective of this paper is to provide an effective algorithm that presents quick and optimized solutions for higher-complexity OBS levels. This algorithm is called Iterative Greedy Algorithm (IGA-OBS), and its performance is compared to the genetic algorithm (GA-OBS) by Pinto and Nagano. Performance evaluations were carried out after intense computational experiments for problems with different complexity levels. The results demonstrate that the GA-OBS is limited to medium-size instances, whereas the IGA-OBS is better adapted to reality, providing OBS with solutions with satisfactory time and quality. The IGA-OBS enables managers to make decisions in a more agile and consistent way in terms of the trade-off between the level of customer service and the maximization of the financial result of DCs. This paper fills a gap in the literature, makes innovative contributions, and provides suggestions for further research aimed at developing more suitable optimization methods for OBS.
Este documento aborda el problema de la Secuenciación Optimizada de Facturación (OBS) para maximizar la facturación de la cartera de pedidos de un centro de distribución (CD) típico. Este es un nuevo problema en la literatura, y la búsqueda de la mejor combinación de facturación ha exigido mejores métodos para optimizar los CD. Por lo tanto, el objetivo de este artículo es proporcionar un algoritmo eficaz que presente soluciones rápidas y optimizadas para niveles más altos de complejidad OBS. Este algoritmo se denomina Algoritmo Voraz Iterativo (IGA-OBS) y su rendimiento se compara con el del algoritmo genético (GA-OBS) de Pinto y Nagano. Se llevaron a cabo evaluaciones de desempeño después de intensos experimentos computacionales para problemas con diferentes niveles de complejidad. Los resultados demuestran que el GA-OBS se limita a instancias de tamaño medio, mientras que el IGA-OBS se adapta mejor a la realidad brindando soluciones en tiempo y calidad satisfactorios a OBS. El IGA-OBS permite a los gerentes tomar decisiones de una manera mas ágil y consistente frente al trade-off entre el nivel de servicio al cliente y la maximización del resultado financiero de los CD. Este artículo llena un vacío en la literatura, aporta contribuciones innovadoras y proporciona sugerencias para futuras investigaciones destinadas a desarrollar métodos de optimización más adecuados para OBS.
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Copyright (c) 2021 Anderson Rogério Faia Pinto, Marcelo Seido Nagano
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