Aplicación de algoritmo AntNet al problema de secuenciación en múltiples máquinas
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Date
2008-06-22
Authors
Suárez Ferreira, Juliett Maybetsy
Journal Title
Journal ISSN
Volume Title
Publisher
Universidad Central “Marta Abreu” de Las Villas
Abstract
Los problemas de Optimización Combinatoria ocupan diversos campos como la economía, el
comercio, la ingeniería, la industria o la medicina. Los problemas de secuenciación, como ejemplo de
estos, consisten en la localización o asignación de recursos en el tiempo a un conjunto de tareas o
actividades; dentro de ellos, aparece el problema de secuenciación en múltiples máquinas,
caracterizado como la actividad de asignar un número de trabajos que son llevados a cabo por un
número de máquinas, con un rendimiento cuya efectividad en costo o tiempo son cumplidos.
En los últimos años ha habido un crecimiento en el desarrollo de procedimientos heurísticos para
resolver problemas de optimización. Este es el caso de la metaheurística ACO (Ant Colony
Optimization) que se inspira en el comportamiento que rige a las hormigas de diversas especies para
encontrar los caminos más cortos entre las fuentes de comida y el hormiguero. El tipo de problemas
que pueden ser resueltos por esta vía pertenecen al grupo de problemas de camino mínimo de
optimización combinatoria, que pueden representarse en forma de grafo ponderado y que ha
demostrado ser capaz de obtener buenos resultados para varios de los problemas a los que ha sido
aplicada.
En este trabajo se presenta una alternativa de solución al problema MMS utilizando AntNet, un
algoritmo aproximado o heurístico perteneciente a ACO, diseñado inicialmente para resolver
problemas de enrutamiento en las redes de telecomunicación. En esta propuesta de solución se
realizan adaptaciones al algoritmo para ajustarlo a las características del problema MMS, tanto en la
representación de la solución como en el procedimiento de búsqueda del valor óptimo y se obtienen
resultados comparables con otra variante de solución encontrada en la literatura, así como con
variantes propias del algoritmo obtenidas por la variación de algunos parámetros.
Combinatorial Optimization problems are present in different fields like economy, commerce, engineering, industry or medicine. Scheduling problems, for example, consist in the allocation in time of resources to jobs or activities, one of the scheduling problems is the Multi Machine Scheduling (MMS), and this is characterized as the activity of assigning a number of jobs to machines such that certain performance demands like cost and time effectiveness are fulfilled. In recent years there has been a growth in the development of heuristic procedures to solve optimization problems. This is the case of the ACO metaheuristic (Ant Colony Optimization), which takes inspiration from the behavior of some ant species to find the shortest path between the food and the nest. The kind of problems that can be solved using this way belongs to the group of problems of minimum path of combinatorial optimization. It is worthwhile to note that ACO algorithms are appropriate for discrete optimization problems that can be characterized as a graph. This metaheuristic has demonstrated that is able to obtain good results for several problems to which it has been applied. In this work an alternative of solution to the MMS problem using AntNet is presented. AntNet is an approximate or heuristic algorithm of the Ant Colony Optimization metaheuristic, it was initially designed to solve routing problems in telecommunication networks. In this solution proposal, some adaptations to the algorithm were introduced in order to approach it to the MMS problem characteristics, these adaptations can be observed in the representation of the solution, as well as in the search procedure of the optimal value. Comparable results with another variant of solution found in the literature are obtained. The results are also comparables with own variants of the algorithm obtained by the variation of some of parameters that take part in the different formulas.
Combinatorial Optimization problems are present in different fields like economy, commerce, engineering, industry or medicine. Scheduling problems, for example, consist in the allocation in time of resources to jobs or activities, one of the scheduling problems is the Multi Machine Scheduling (MMS), and this is characterized as the activity of assigning a number of jobs to machines such that certain performance demands like cost and time effectiveness are fulfilled. In recent years there has been a growth in the development of heuristic procedures to solve optimization problems. This is the case of the ACO metaheuristic (Ant Colony Optimization), which takes inspiration from the behavior of some ant species to find the shortest path between the food and the nest. The kind of problems that can be solved using this way belongs to the group of problems of minimum path of combinatorial optimization. It is worthwhile to note that ACO algorithms are appropriate for discrete optimization problems that can be characterized as a graph. This metaheuristic has demonstrated that is able to obtain good results for several problems to which it has been applied. In this work an alternative of solution to the MMS problem using AntNet is presented. AntNet is an approximate or heuristic algorithm of the Ant Colony Optimization metaheuristic, it was initially designed to solve routing problems in telecommunication networks. In this solution proposal, some adaptations to the algorithm were introduced in order to approach it to the MMS problem characteristics, these adaptations can be observed in the representation of the solution, as well as in the search procedure of the optimal value. Comparable results with another variant of solution found in the literature are obtained. The results are also comparables with own variants of the algorithm obtained by the variation of some of parameters that take part in the different formulas.
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Keywords
Algoritmo AntNet, Problema de Secuenciación, Múltiples Máquinas, Inteligencia Artificial