Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/3124
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHendrik Setia Budi, Marischa Elveny-
dc.contributor.authorPavel Zhuravlev, Abduladheem Turki Jalil-
dc.contributor.authorSamaher Al-Janabi, Ayad F. Alkaim-
dc.contributor.authorMarwan Mahmood Saleh, Rustem Adamovich Shichiyakh-
dc.contributor.authorSutarto-
dc.date.accessioned2022-10-19T09:53:03Z-
dc.date.available2022-10-19T09:53:03Z-
dc.date.issued2022-
dc.identifier.issnISSN 0867-6356 e-ISSN 2300-3405-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/3124-
dc.description.abstractThe problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents an efficient method for optimizing the problem of unequal facility layouts. In this method, the genetic algorithm is improved and developed into an adaptive genetic algorithm. In this algorithm, the mutation operator is applied only when the similarity of chromosomes in each population reaches a certain level. This intelligence prevents jumps in situations where they are not needed and reduces computational time. In order to measure the performance of the proposed algorithm, its performance is compared with the performance of conventional genetic algorithms and refrigeration simulators. Computational results show that the adaptive genetic algorithm is able to achieve higher-quality solutions.en_US
dc.publisherF O U N D A T I O N S O F C O M P U T I N G A N D D E C I S I O N S C I E N C E Sen_US
dc.subject: Unequal Facility locationen_US
dc.subjectInteractionsen_US
dc.subjectAdaptive Genetic Algorithmen_US
dc.subjectMutation Operator Intelligence,en_US
dc.subjecthealthy lifestyleen_US
dc.titleDevelopment of an Adaptive Genetic Algorithm to Optimize the Problem Of Unequal Facility Locationen_US
dc.typeArticleen_US
Appears in Collections:قسم الفيزياء الحياتية

Files in This Item:
File Description SizeFormat 
genetic algorthim.2478_fcds-2022-0006.pdf1.31 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.