A genetic algorithm approach for the TV self-promotion assignment problem
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | , |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/11805 |
Resumo: | We report on the development of a Genetic Algorithm (GA), which has been integrated into a Decision Support System to plan the best assignment of the weekly self-promotion space for a TV station. The problem addressed consists on deciding which shows to advertise and when such that the number of viewers, of an intended group or target, is maximized. The GA proposed incorporates a greedy heuristic to find good initial solutions. These solutions, as well as the solutions later obtained through the use of the GA, go then through a repair procedure. This is used with two objectives, which are addressed in turn. Firstly, it checks the solution feasibility and if unfeasible it is fixed by removing some shows. Secondly, it tries to improve the solution by adding some extra shows. Since the problem faced by the commercial TV station is too big and has too many features it cannot be solved exactly. Therefore, in order to test the quality of the solutions provided by the proposed GA we have randomly generated some smaller problem instances. For these problems we have obtained solutions on average within 1% of the optimal solution value. |
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A genetic algorithm approach for the TV self-promotion assignment problemGenetic AlgorithmsCombinatorial optimizationImage processingNumerical optimizationTV self-promotionAssignment problemTV Self-Promotion Assignment ProblemScience & TechnologyWe report on the development of a Genetic Algorithm (GA), which has been integrated into a Decision Support System to plan the best assignment of the weekly self-promotion space for a TV station. The problem addressed consists on deciding which shows to advertise and when such that the number of viewers, of an intended group or target, is maximized. The GA proposed incorporates a greedy heuristic to find good initial solutions. These solutions, as well as the solutions later obtained through the use of the GA, go then through a repair procedure. This is used with two objectives, which are addressed in turn. Firstly, it checks the solution feasibility and if unfeasible it is fixed by removing some shows. Secondly, it tries to improve the solution by adding some extra shows. Since the problem faced by the commercial TV station is too big and has too many features it cannot be solved exactly. Therefore, in order to test the quality of the solutions provided by the proposed GA we have randomly generated some smaller problem instances. For these problems we have obtained solutions on average within 1% of the optimal solution value.AIP PublishingUniversidade do MinhoPereira, P. A.Fontes, Fernando A. C. C.Fontes, Dalila B. M. M.20092009-01-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/11805engSIMOS, Theodore E. ; PSIHOYIOS, George ; TSITOURAS, Ch., eds – “Numerical Analysis and Applied Mathematics : International Conference on Numerical Analysis and Applied Mathematics, Rethymno, Crete (Greece), 2009.” Melville : American Institute of Physics, 2009. ISBN 978-0-7354-0709. vol. 2, p. 1378-1381.978-0-7354-07090094-243X10.1063/1.3241343http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=APCPCS001168000001001378000001&idtype=cvips&gifs=yes&ref=noinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-11T06:08:58Zoai:repositorium.sdum.uminho.pt:1822/11805Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-11T06:08:58Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A genetic algorithm approach for the TV self-promotion assignment problem |
title |
A genetic algorithm approach for the TV self-promotion assignment problem |
spellingShingle |
A genetic algorithm approach for the TV self-promotion assignment problem Pereira, P. A. Genetic Algorithms Combinatorial optimization Image processing Numerical optimization TV self-promotion Assignment problem TV Self-Promotion Assignment Problem Science & Technology |
title_short |
A genetic algorithm approach for the TV self-promotion assignment problem |
title_full |
A genetic algorithm approach for the TV self-promotion assignment problem |
title_fullStr |
A genetic algorithm approach for the TV self-promotion assignment problem |
title_full_unstemmed |
A genetic algorithm approach for the TV self-promotion assignment problem |
title_sort |
A genetic algorithm approach for the TV self-promotion assignment problem |
author |
Pereira, P. A. |
author_facet |
Pereira, P. A. Fontes, Fernando A. C. C. Fontes, Dalila B. M. M. |
author_role |
author |
author2 |
Fontes, Fernando A. C. C. Fontes, Dalila B. M. M. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Pereira, P. A. Fontes, Fernando A. C. C. Fontes, Dalila B. M. M. |
dc.subject.por.fl_str_mv |
Genetic Algorithms Combinatorial optimization Image processing Numerical optimization TV self-promotion Assignment problem TV Self-Promotion Assignment Problem Science & Technology |
topic |
Genetic Algorithms Combinatorial optimization Image processing Numerical optimization TV self-promotion Assignment problem TV Self-Promotion Assignment Problem Science & Technology |
description |
We report on the development of a Genetic Algorithm (GA), which has been integrated into a Decision Support System to plan the best assignment of the weekly self-promotion space for a TV station. The problem addressed consists on deciding which shows to advertise and when such that the number of viewers, of an intended group or target, is maximized. The GA proposed incorporates a greedy heuristic to find good initial solutions. These solutions, as well as the solutions later obtained through the use of the GA, go then through a repair procedure. This is used with two objectives, which are addressed in turn. Firstly, it checks the solution feasibility and if unfeasible it is fixed by removing some shows. Secondly, it tries to improve the solution by adding some extra shows. Since the problem faced by the commercial TV station is too big and has too many features it cannot be solved exactly. Therefore, in order to test the quality of the solutions provided by the proposed GA we have randomly generated some smaller problem instances. For these problems we have obtained solutions on average within 1% of the optimal solution value. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009 2009-01-01T00:00:00Z |
dc.type.driver.fl_str_mv |
conference paper |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/11805 |
url |
http://hdl.handle.net/1822/11805 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
SIMOS, Theodore E. ; PSIHOYIOS, George ; TSITOURAS, Ch., eds – “Numerical Analysis and Applied Mathematics : International Conference on Numerical Analysis and Applied Mathematics, Rethymno, Crete (Greece), 2009.” Melville : American Institute of Physics, 2009. ISBN 978-0-7354-0709. vol. 2, p. 1378-1381. 978-0-7354-0709 0094-243X 10.1063/1.3241343 http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=APCPCS001168000001001378000001&idtype=cvips&gifs=yes&ref=no |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
AIP Publishing |
publisher.none.fl_str_mv |
AIP Publishing |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
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1817544869193711616 |