Algoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop scheduling
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da Uninove |
Texto Completo: | http://bibliotecatede.uninove.br/handle/tede/2577 |
Resumo: | This work deals with the scheduling problem in a job shop environment, known in the international literature as Job Shop Scheduling Problem (JSSP). Due to their computational complexity, metaheuristics have been commonly employed in their solution, but performance comparable to the state-of-the-art depends on upon an e cient exploration of the solution space characteristics of this problem, through the tness landscape analysis. Brie y, the tness landscape represents a space landscape of the generated solutions - formed by the solutions in space, their tness values, and a notion of neighborhood - and its analysis provides relevant information for improving the optimization method. Thus the objective of this work is the development of a Hybrid Genetic Algorithm (HGA) with diversi cation and intensi cation strategies based on the tness landscape Principal Component Analysis for the Job Shop Scheduling Problem. Therefore, a method based on Principal Component Analysis (PCA) is proposed to evaluate diversity characteristics of solutions of a population, classifying each individual based on their similarity to the others. This classi cation determines an individual contribution rate that is used to select similar solutions that can be substituted in the diversi cation stage. When dealing with suboptimal solutions, the replacement of the selected individual is done considering the best solution found in the intensi cation step. In this case, a Bidimensional Binary Genetic Algorithm (GAB) is used to extend the search region beyond the neighborhood of the selected individual in search of a better solution. Finally, the initial and nal solutions are reconnected and the best solution in the path between them is inserted into the population instead of the one selected by the PCA. Comparing the HGA with a local search method the results show that there was improvement in several instances both in reducing the makespan value and in the number of generations of the algorithm. In relation to other approaches referenced in the literature, HGA obtained competitive results for some instances, but it is still necessary to re ne the method for larger problems, which is dependent on the adequate choice of HGA parameters and the selection of initial solutions for intensification application. |
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Pereira, Fabio HenriquePereira, Fabio HenriqueFerreira, DeisemaraFavaretto, FabioSassi, Renato JoseAraujo, Sidnei Alves dehttp://lattes.cnpq.br/1399302664820863Rosa, Aparecida de Fátima Castello2021-10-05T14:14:10Z2019-07-01Rosa, Aparecida de Fátima Castello. Algoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop scheduling. 2019. 137 f. Tese( Programa de Pós-Graduação em Informática e Gestão do Conhecimento) - Universidade Nove de Julho, São Paulo.http://bibliotecatede.uninove.br/handle/tede/2577This work deals with the scheduling problem in a job shop environment, known in the international literature as Job Shop Scheduling Problem (JSSP). Due to their computational complexity, metaheuristics have been commonly employed in their solution, but performance comparable to the state-of-the-art depends on upon an e cient exploration of the solution space characteristics of this problem, through the tness landscape analysis. Brie y, the tness landscape represents a space landscape of the generated solutions - formed by the solutions in space, their tness values, and a notion of neighborhood - and its analysis provides relevant information for improving the optimization method. Thus the objective of this work is the development of a Hybrid Genetic Algorithm (HGA) with diversi cation and intensi cation strategies based on the tness landscape Principal Component Analysis for the Job Shop Scheduling Problem. Therefore, a method based on Principal Component Analysis (PCA) is proposed to evaluate diversity characteristics of solutions of a population, classifying each individual based on their similarity to the others. This classi cation determines an individual contribution rate that is used to select similar solutions that can be substituted in the diversi cation stage. When dealing with suboptimal solutions, the replacement of the selected individual is done considering the best solution found in the intensi cation step. In this case, a Bidimensional Binary Genetic Algorithm (GAB) is used to extend the search region beyond the neighborhood of the selected individual in search of a better solution. Finally, the initial and nal solutions are reconnected and the best solution in the path between them is inserted into the population instead of the one selected by the PCA. Comparing the HGA with a local search method the results show that there was improvement in several instances both in reducing the makespan value and in the number of generations of the algorithm. In relation to other approaches referenced in the literature, HGA obtained competitive results for some instances, but it is still necessary to re ne the method for larger problems, which is dependent on the adequate choice of HGA parameters and the selection of initial solutions for intensification application.Este trabalho trata do problema de scheduling em ambiente de produção do tipo job shop, conhecido na literatura internacional por Job Shop Scheduling Problem (JSSP). Em função da sua complexidade computacional, metaheurísticas têm sido comumente empregadas em sua solução, mas um desempenho comparável ao estado da arte depende de uma exploração e ciente das características do espaço de soluções desse problema, o que pode ser realizado por meio da análise do tness landscape. Resumidamente, o tness landscape representa uma paisagem do espaço das soluções geradas - formada pelas soluções no espaço, seus valores de aptidão, e uma noção de vizinhança - e sua analise fornece informações relevantes para o aprimoramento do método de otimização. Assim o objetivo deste trabalho é o desenvolvimento de um Algoritmo Genético Híbrido (HGA) com estrat égias de diversi cação e intensi cação baseadas na Análise de Componentes Principais do tness landscape para o problema de Job Shop Scheduling. Para tal, é proposto um mé- todo baseado na Análise de Componentes Principais (PCA) para avaliar características de diversidade das soluções de uma população, classi cando cada indivíduo com base em sua semelhança aos demais. Essa classi cação determina um índice individual de contribuição que é utilizado para selecionar soluções semelhantes que poderão ser substituídas na etapa de diversi cação. Quando se tratam de soluções subótimas, a substituição do indivíduo selecionado é feita considerando a melhor solução encontrada na etapa de intensi cação. Nesse caso, um Algoritmo Genético Binário Bidimensional (GAB) é utilizado para ampliar a região de busca para além da vizinhança do indivíduo selecionado a procura de uma melhor solução. Por m, as soluções inicial e nal são reconectadas e a melhor solução no caminho entre elas é inserida na população em substituição àquela selecionada pela PCA. Comparando-se o HGA com um método de busca local os resultados mostram que houve melhora em várias instâncias tanto na redução do valor de makespan quanto em número de gerações do algoritmo. Em relação a outras abordagens referenciadas na literatura, o HGA obteve resultados competitivos para algumas instâncias, mas ainda se faz necessário um re namento no método para problemas de maior dimensão, o qual é dependente da escolha adequada dos parâmetros do HGA e na seleção das soluções iniciais para aplicação da intensificação.Submitted by Nadir Basilio (nadirsb@uninove.br) on 2021-10-05T14:14:10Z No. of bitstreams: 1 Aparecida de Fátima Castello Rosa.pdf: 2180526 bytes, checksum: a156a7c0849d381a8cc2159c9ebecb18 (MD5)Made available in DSpace on 2021-10-05T14:14:10Z (GMT). No. of bitstreams: 1 Aparecida de Fátima Castello Rosa.pdf: 2180526 bytes, checksum: a156a7c0849d381a8cc2159c9ebecb18 (MD5) Previous issue date: 2019-07-01application/pdfporUniversidade Nove de JulhoPrograma de Pós-Graduação em Informática e Gestão do ConhecimentoUNINOVEBrasilInformáticaalgoritmo genético híbridojob shop scheduling problemfitness landscapediversificação e intensificaçãoanálise de componentes principaispath relinkinghybrid genetic algorithmjob shop scheduling problemfitness landscapediversification and intensificationprincipal component analysispath relinkingCIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAOAlgoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop schedulinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis8930092515683771531600info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da Uninoveinstname:Universidade Nove de Julho (UNINOVE)instacron:UNINOVEORIGINALAparecida de Fátima Castello Rosa.pdfAparecida de Fátima Castello Rosa.pdfapplication/pdf2180526http://localhost:8080/tede/bitstream/tede/2577/2/Aparecida+de+F%C3%A1tima+Castello+Rosa.pdfa156a7c0849d381a8cc2159c9ebecb18MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://localhost:8080/tede/bitstream/tede/2577/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede/25772024-06-06 14:22:02.585oai:localhost: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Biblioteca Digital de Teses e Dissertaçõeshttp://bibliotecatede.uninove.br/PRIhttp://bibliotecatede.uninove.br/oai/requestbibliotecatede@uninove.br||bibliotecatede@uninove.bropendoar:2024-06-06T17:22:02Biblioteca Digital de Teses e Dissertações da Uninove - Universidade Nove de Julho (UNINOVE)false |
dc.title.por.fl_str_mv |
Algoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop scheduling |
title |
Algoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop scheduling |
spellingShingle |
Algoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop scheduling Rosa, Aparecida de Fátima Castello algoritmo genético híbrido job shop scheduling problem fitness landscape diversificação e intensificação análise de componentes principais path relinking hybrid genetic algorithm job shop scheduling problem fitness landscape diversification and intensification principal component analysis path relinking CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO |
title_short |
Algoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop scheduling |
title_full |
Algoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop scheduling |
title_fullStr |
Algoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop scheduling |
title_full_unstemmed |
Algoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop scheduling |
title_sort |
Algoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop scheduling |
author |
Rosa, Aparecida de Fátima Castello |
author_facet |
Rosa, Aparecida de Fátima Castello |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Pereira, Fabio Henrique |
dc.contributor.referee1.fl_str_mv |
Pereira, Fabio Henrique |
dc.contributor.referee2.fl_str_mv |
Ferreira, Deisemara |
dc.contributor.referee3.fl_str_mv |
Favaretto, Fabio |
dc.contributor.referee4.fl_str_mv |
Sassi, Renato Jose |
dc.contributor.referee5.fl_str_mv |
Araujo, Sidnei Alves de |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/1399302664820863 |
dc.contributor.author.fl_str_mv |
Rosa, Aparecida de Fátima Castello |
contributor_str_mv |
Pereira, Fabio Henrique Pereira, Fabio Henrique Ferreira, Deisemara Favaretto, Fabio Sassi, Renato Jose Araujo, Sidnei Alves de |
dc.subject.por.fl_str_mv |
algoritmo genético híbrido job shop scheduling problem fitness landscape diversificação e intensificação análise de componentes principais path relinking |
topic |
algoritmo genético híbrido job shop scheduling problem fitness landscape diversificação e intensificação análise de componentes principais path relinking hybrid genetic algorithm job shop scheduling problem fitness landscape diversification and intensification principal component analysis path relinking CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO |
dc.subject.eng.fl_str_mv |
hybrid genetic algorithm job shop scheduling problem fitness landscape diversification and intensification principal component analysis path relinking |
dc.subject.cnpq.fl_str_mv |
CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO |
description |
This work deals with the scheduling problem in a job shop environment, known in the international literature as Job Shop Scheduling Problem (JSSP). Due to their computational complexity, metaheuristics have been commonly employed in their solution, but performance comparable to the state-of-the-art depends on upon an e cient exploration of the solution space characteristics of this problem, through the tness landscape analysis. Brie y, the tness landscape represents a space landscape of the generated solutions - formed by the solutions in space, their tness values, and a notion of neighborhood - and its analysis provides relevant information for improving the optimization method. Thus the objective of this work is the development of a Hybrid Genetic Algorithm (HGA) with diversi cation and intensi cation strategies based on the tness landscape Principal Component Analysis for the Job Shop Scheduling Problem. Therefore, a method based on Principal Component Analysis (PCA) is proposed to evaluate diversity characteristics of solutions of a population, classifying each individual based on their similarity to the others. This classi cation determines an individual contribution rate that is used to select similar solutions that can be substituted in the diversi cation stage. When dealing with suboptimal solutions, the replacement of the selected individual is done considering the best solution found in the intensi cation step. In this case, a Bidimensional Binary Genetic Algorithm (GAB) is used to extend the search region beyond the neighborhood of the selected individual in search of a better solution. Finally, the initial and nal solutions are reconnected and the best solution in the path between them is inserted into the population instead of the one selected by the PCA. Comparing the HGA with a local search method the results show that there was improvement in several instances both in reducing the makespan value and in the number of generations of the algorithm. In relation to other approaches referenced in the literature, HGA obtained competitive results for some instances, but it is still necessary to re ne the method for larger problems, which is dependent on the adequate choice of HGA parameters and the selection of initial solutions for intensification application. |
publishDate |
2019 |
dc.date.issued.fl_str_mv |
2019-07-01 |
dc.date.accessioned.fl_str_mv |
2021-10-05T14:14:10Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
Rosa, Aparecida de Fátima Castello. Algoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop scheduling. 2019. 137 f. Tese( Programa de Pós-Graduação em Informática e Gestão do Conhecimento) - Universidade Nove de Julho, São Paulo. |
dc.identifier.uri.fl_str_mv |
http://bibliotecatede.uninove.br/handle/tede/2577 |
identifier_str_mv |
Rosa, Aparecida de Fátima Castello. Algoritmo genético híbrido baseado na análise de componentes principais do fitness landscape para o problema de job shop scheduling. 2019. 137 f. Tese( Programa de Pós-Graduação em Informática e Gestão do Conhecimento) - Universidade Nove de Julho, São Paulo. |
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http://bibliotecatede.uninove.br/handle/tede/2577 |
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por |
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openAccess |
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Programa de Pós-Graduação em Informática e Gestão do Conhecimento |
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UNINOVE |
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