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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yenisey, Mehmet Mutlu | - |
dc.date.accessioned | 2022-01-13T06:12:39Z | - |
dc.date.available | 2022-01-13T06:12:39Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | Yağmahan, B. ve Yenisey, M. M. (2009). "Scheduling practice and recent developments in flow shop and job shop scheduling". Computational Intelligence in Flow Shop and Job Shop Scheduling, Studies in Computational Intelligence, 230, 261-300. | en_US |
dc.identifier.issn | 1860-949X | - |
dc.identifier.uri | https://doi.org/10.1007/978-3-642-02836-6_9 | - |
dc.identifier.uri | https://link.springer.com/chapter/10.1007%2F978-3-642-02836-6_9 | - |
dc.identifier.uri | http://hdl.handle.net/11452/24055 | - |
dc.description.abstract | Each plant and/or service provider performs several tasks to satisfy customer demand. Every task consumes several resources in order to be completed. Scheduling deals with the allocation of limited resources to tasks over time. Because the resources used in manufacturing activities are very limited, scheduling becomes a very important concept in managerial decision-making. This importance draws the attention of both practitioners and academicians to scheduling. Scheduling problems usually lie in the NP-hard problem class. Difficulty especially increases as the number of jobs or machines involved increases. As the problem size increases, exact solution techniques become insufficient. This chapter provides an overview of recent developments in computational intelligence approaches to flow shop and job shop scheduling. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Particle swarm optimization | en_US |
dc.subject | Tabu search algorithm | en_US |
dc.subject | Genetic algorithm | en_US |
dc.subject | Heuristic algorithm | en_US |
dc.subject | Total tardiness | en_US |
dc.subject | Scatter search | en_US |
dc.subject | M-machine | en_US |
dc.subject | N-job | en_US |
dc.subject | Minimizing makespan | en_US |
dc.subject | Sequencing problem | en_US |
dc.subject | Computer science | en_US |
dc.title | Scheduling practice and recent developments in flow shop and job shop scheduling | en_US |
dc.type | Article | en_US |
dc.type | Book Chapter | en_US |
dc.identifier.wos | 000270008400009 | tr_TR |
dc.identifier.scopus | 2-s2.0-78049242014 | tr_TR |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi | tr_TR |
dc.contributor.department | Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü. | tr_TR |
dc.contributor.orcid | 0000-0003-1744-3062 | tr_TR |
dc.identifier.startpage | 261 | tr_TR |
dc.identifier.endpage | 300 | tr_TR |
dc.identifier.volume | 230 | tr_TR |
dc.relation.journal | Computational Intelligence in Flow Shop and Job Shop Scheduling, Studies in Computational Intelligence | en_US |
dc.contributor.buuauthor | Yağmahan, Betül | - |
dc.contributor.researcherid | B-5557-2017 | tr_TR |
dc.relation.collaboration | Yurt içi | tr_TR |
dc.subject.wos | Computer science, artificial intelligence | en_US |
dc.indexed.wos | BKCIS | en_US |
dc.indexed.scopus | Scopus | en_US |
dc.contributor.scopusid | 23487445600 | tr_TR |
dc.subject.scopus | Flow Shop Scheduling; Permutation Flowshop; No-Wait | en_US |
Appears in Collections: | Scopus Web of Science |
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