Heurísticas de agrupación híbridas eficientes para el problema de empacado de objetos en contenedores
Efficient Hybrid Grouping Heuristics for the Bin Packing Problem
Laura Cruz-Reyes1, Marcela Quiroz C.1, Adriana C. F. Alvim2, Héctor J. Fraire Huacuja1, Claudia Gómez S.1 y José Torres-Jiménez3
1 Instituto Tecnológico de Ciudad Madero, México lauracruzreyes@itcm.edu.mx, qc.marcela@gmail.com, cggs71@hotmail.com
]]> 2 Universidad Federal do Estado do Rio de Janeiro, Brasil adriana@uniriotec.br3 Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, México jtj@tamps.cinvestav.mx, automatas2002@yahoo.com.mx
Artículo recibido el 03/01/2011;
aceptado el 08/11/2011.
Resumen
En este artículo se aborda un problema clásico muy conocido por su aplicabilidad y complejidad: el empacado de objetos en contenedores (Bin Packing Problem, BPP). Para la solución de BPP se propone un algoritmo genético híbrido de agrupación denominado HGGA-BP. El algoritmo propuesto está inspirado en el esquema de representación de grupos de Falkenauer, el cual aplica operadores evolutivos a nivel de contenedores. HGGA-BP incluye heurísticas eficientes para generar la población inicial y realizar mutación y cruzamiento de grupos; así como estrategias híbridas para el acomodo de objetos que quedaron libres al aplicar los operadores grupales. La efectividad del algoritmo es comparable con la de los mejores del estado del arte, superando los resultados publicados para el conjunto de instancias hard28, el cual ha mostrado el mayor grado de dificultad para los algoritmos de solución de BPP.
Palabras clave. Metodologías computacionales, inteligencia artificial, solución de problemas, problema de empacado de objeto en contenedores, algoritmo genético hibrido.
]]> Abstract
This article addresses a classical problem known for its applicability and complexity: the Bin Packing Problem (BPP). A hybrid grouping genetic algorithm called HGGA-BP is proposed to solve BPP. The proposed algorithm is inspired by the Falkenauer grouping encoding scheme, which applies evolutionary operators at the bin level. HGGA-BP includes efficient heuristics to genérate the initial population and performs mutation and crossover for groups as well as hybrid strategies for the arrangement of objects that were released by the group operators. The effectiveness of the algorithm is comparable with the best state-of-the-art algorithms, outperforming the published results for the class of instances hard28, which has shown the highest difficulty for algorithms that solve BPP.
Keywords: Computer methodologies, artificial intelligence, problem solving, bin packing problem, hybrid genetic algorithm.
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