Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/16920
Título: | A hybrid genetic algorithm for constrained combinatorial problems : an application to promotion planning problem |
Autor(es): | Pereira, Paulo A. A. Fontes, Fernando A. C. C. Fontes, Dalila B. M. M. |
Palavras-chave: | Genetic algorithms Combinatorial optimization TV self-promotion Assignment problem TV Self-Promotion Assignment Problem |
Data: | Jan-2011 |
Editora: | Springer Verlag |
Revista: | Operations Research Proceedings |
Resumo(s): | We propose a Hybrid Genetic Algorithm (HGA) for a combinatorial optimization problem, motivated by, and a simplification of, a TV Self-promotion Assignment Problem. Given the weekly self-promotion space (a set of TV breaks with known duration) and a set of products to promote, the problem consists of assigning the products to the breaks in the “best” possible way. The objective is to maximize contacts in the target audience for each product, whist satisfying all constraints. The HGA developed incorporates a greedy heuristic to initialize part of the population and uses a repair routine to guarantee feasibility of each member of the population. The HGA works on a simplified version of the problem that, nevertheless, maintains its essential features. The proposed simplified problem is a binary programming problem that has similarities with other known combinatorial optimization problems, such as the assignment problem or the multiple knapsack problem, but has some distinctive features that characterize it as a new problem. Although we are mainly interested in solving problems of large dimension (of about 200 breaks and 50 spots), the quality of the solution has been tested on smaller dimension problems for which we are able to find an exact global minimum using a branch-and-bound algorithm. For these smaller dimension problems we have obtained solutions, on average, within 1% of the optimal solution value. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/16920 |
ISBN: | 9783642200083 |
DOI: | 10.1007/978-3-642-20009-0_48 |
ISSN: | 0721-5924 |
Versão da editora: | www.springerlink.com |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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OR-2010-HybridGA.pdf Acesso restrito! | Documento principal | 83,76 kB | Adobe PDF | Ver/Abrir |