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Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems
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 incre...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274337/ http://dx.doi.org/10.1007/978-3-030-50146-4_19 |
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author | Ferreira, Inês C. Firme, Bernardo Martins, Miguel S. E. Coito, Tiago Viegas, Joaquim Figueiredo, João Vieira, Susana M. Sousa, João M. C. |
author_facet | Ferreira, Inês C. Firme, Bernardo Martins, Miguel S. E. Coito, Tiago Viegas, Joaquim Figueiredo, João Vieira, Susana M. Sousa, João M. C. |
author_sort | Ferreira, Inês C. |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-7274337 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72743372020-06-05 Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems Ferreira, Inês C. Firme, Bernardo Martins, Miguel S. E. Coito, Tiago Viegas, Joaquim Figueiredo, João Vieira, Susana M. Sousa, João M. C. Information Processing and Management of Uncertainty in Knowledge-Based Systems Article 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. 2020-05-18 /pmc/articles/PMC7274337/ http://dx.doi.org/10.1007/978-3-030-50146-4_19 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Ferreira, Inês C. Firme, Bernardo Martins, Miguel S. E. Coito, Tiago Viegas, Joaquim Figueiredo, João Vieira, Susana M. Sousa, João M. C. Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems |
title | 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_short | Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems |
title_sort | artificial bee colony algorithm applied to dynamic flexible job shop problems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274337/ http://dx.doi.org/10.1007/978-3-030-50146-4_19 |
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