Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems

Detalhes bibliográficos
Autor(a) principal: Ferreira, I.
Data de Publicação: 2020
Outros Autores: Firme, B., Martins, M., Coito, T., Viegas, J., Figueiredo, Joao, Vieira, S., Sousa, J.
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/28823
https://doi.org/FERREIRA, I., FIRME, B., MARTINS, M., COITO, T., VIEGAS, J., FIGUEIREDO, J., VIEIRA, S. SOUSA, J. [2020] Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems. In: Lesot MJ. et al. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020. Communications in Computer and Information Science, vol 1237. Springer, Cham. https://doi.org/10.1007/978-3-030-50146-4_19
https://doi.org/10.1007/978-3-030-50146-4_19
Resumo: This work introduces a scheduling technique using the Artificial Bee Colony (ABC) algorithm for static and dynamic environments. The ABC algorithm combines different initial populations and generation of new food source methods, including a moving operations technique and a local search method increasing the variable neighbourhood search that, as a result, improves the solution quality. The algorithm is validated and its performance is tested in a static environment in 9 instances of Flexible Job Shop Problem (FJSP) from Brandimarte dataset obtaining in 5 instances the best known for the instance under study and a new best known in instance mk05. The work also focus in developing tools to process the information on the factory through the development of solutions when facing disruptions and dynamic events. Three real-time events are considered on the dynamic environment: jobs cancellation, operations cancellation and new jobs arrival. Two scenarios are studied for each real-time event: the first situation considers the minimization of the disruption between the previous schedule and the new one and the second situation generates a completely new schedule after the occurrence. Summarizing, six adaptations of ABC algorithm are created to solve dynamic environment scenarios and their performances are compared with the benchmarks of two case studies outperforming both.
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spelling Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop ProblemsDynamic environmentNew jobs arrivalOperations cancellationJobs cancellationFlexible job shop reschedulingThis work introduces a scheduling technique using the Artificial Bee Colony (ABC) algorithm for static and dynamic environments. The ABC algorithm combines different initial populations and generation of new food source methods, including a moving operations technique and a local search method increasing the variable neighbourhood search that, as a result, improves the solution quality. The algorithm is validated and its performance is tested in a static environment in 9 instances of Flexible Job Shop Problem (FJSP) from Brandimarte dataset obtaining in 5 instances the best known for the instance under study and a new best known in instance mk05. The work also focus in developing tools to process the information on the factory through the development of solutions when facing disruptions and dynamic events. Three real-time events are considered on the dynamic environment: jobs cancellation, operations cancellation and new jobs arrival. Two scenarios are studied for each real-time event: the first situation considers the minimization of the disruption between the previous schedule and the new one and the second situation generates a completely new schedule after the occurrence. Summarizing, six adaptations of ABC algorithm are created to solve dynamic environment scenarios and their performances are compared with the benchmarks of two case studies outperforming both.ELSEVIER2021-01-25T12:39:14Z2021-01-252020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/28823https://doi.org/FERREIRA, I., FIRME, B., MARTINS, M., COITO, T., VIEGAS, J., FIGUEIREDO, J., VIEIRA, S. SOUSA, J. [2020] Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems. In: Lesot MJ. et al. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020. Communications in Computer and Information Science, vol 1237. Springer, Cham. https://doi.org/10.1007/978-3-030-50146-4_19https://doi.org/10.1007/978-3-030-50146-4_19http://hdl.handle.net/10174/28823https://doi.org/10.1007/978-3-030-50146-4_19porndndndndndjfig@uevora.ptndnd285Ferreira, I.Firme, B.Martins, M.Coito, T.Viegas, J.Figueiredo, JoaoVieira, S.Sousa, J.info: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:RCAAP2024-01-03T19:25:04Zoai:dspace.uevora.pt:10174/28823Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:18:29.525929Repositório 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 Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems
title Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems
spellingShingle Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems
Ferreira, I.
Dynamic environment
New jobs arrival
Operations cancellation
Jobs cancellation
Flexible job shop rescheduling
title_short Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems
title_full Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems
title_fullStr Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems
title_full_unstemmed Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems
title_sort Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems
author Ferreira, I.
author_facet Ferreira, I.
Firme, B.
Martins, M.
Coito, T.
Viegas, J.
Figueiredo, Joao
Vieira, S.
Sousa, J.
author_role author
author2 Firme, B.
Martins, M.
Coito, T.
Viegas, J.
Figueiredo, Joao
Vieira, S.
Sousa, J.
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Ferreira, I.
Firme, B.
Martins, M.
Coito, T.
Viegas, J.
Figueiredo, Joao
Vieira, S.
Sousa, J.
dc.subject.por.fl_str_mv Dynamic environment
New jobs arrival
Operations cancellation
Jobs cancellation
Flexible job shop rescheduling
topic Dynamic environment
New jobs arrival
Operations cancellation
Jobs cancellation
Flexible job shop rescheduling
description This work introduces a scheduling technique using the Artificial Bee Colony (ABC) algorithm for static and dynamic environments. The ABC algorithm combines different initial populations and generation of new food source methods, including a moving operations technique and a local search method increasing the variable neighbourhood search that, as a result, improves the solution quality. The algorithm is validated and its performance is tested in a static environment in 9 instances of Flexible Job Shop Problem (FJSP) from Brandimarte dataset obtaining in 5 instances the best known for the instance under study and a new best known in instance mk05. The work also focus in developing tools to process the information on the factory through the development of solutions when facing disruptions and dynamic events. Three real-time events are considered on the dynamic environment: jobs cancellation, operations cancellation and new jobs arrival. Two scenarios are studied for each real-time event: the first situation considers the minimization of the disruption between the previous schedule and the new one and the second situation generates a completely new schedule after the occurrence. Summarizing, six adaptations of ABC algorithm are created to solve dynamic environment scenarios and their performances are compared with the benchmarks of two case studies outperforming both.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01T00:00:00Z
2021-01-25T12:39:14Z
2021-01-25
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10174/28823
https://doi.org/FERREIRA, I., FIRME, B., MARTINS, M., COITO, T., VIEGAS, J., FIGUEIREDO, J., VIEIRA, S. SOUSA, J. [2020] Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems. In: Lesot MJ. et al. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020. Communications in Computer and Information Science, vol 1237. Springer, Cham. https://doi.org/10.1007/978-3-030-50146-4_19
https://doi.org/10.1007/978-3-030-50146-4_19
http://hdl.handle.net/10174/28823
https://doi.org/10.1007/978-3-030-50146-4_19
url http://hdl.handle.net/10174/28823
https://doi.org/FERREIRA, I., FIRME, B., MARTINS, M., COITO, T., VIEGAS, J., FIGUEIREDO, J., VIEIRA, S. SOUSA, J. [2020] Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems. In: Lesot MJ. et al. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020. Communications in Computer and Information Science, vol 1237. Springer, Cham. https://doi.org/10.1007/978-3-030-50146-4_19
https://doi.org/10.1007/978-3-030-50146-4_19
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