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Başlık: Solution method for a large-scale loom scheduling problem with machine eligibility and splitting property
Yazarlar: Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.
Eroǧlu, Duygu Yılmaz
Özmutlu, Hüseyin Cenk
ABH-5209-2020
AAH-1079-2021
56120864000
6603061328
Anahtar kelimeler: Materials science
Genetic algorithms
Large-scale problem
Loom
Scheduling
Sequence-dependent setup
Genetic-algorithm
Parallel machines
Programming approach
Minimizing makespan
Times
Jobs
Restrictions
Constraints
Algorithms
Chromosomes
Looms
Problem solving
Genetic algorithms
Optimization
Textile industry
Chromosome structure
Hybrid genetic algorithms
Large-scale problem
Machine eligibility
Machine eligibility constraint
Scheduling problem
Sequence-dependent setup time
Unrelated parallel machines
Job shop scheduling
Yayın Tarihi: 3-Nis-2017
Yayıncı: Taylor & Francis
Atıf: Eroğlu, D. Y. ve Özmutlu, H. C. (2017). ''Solution method for a large-scale loom scheduling problem with machine eligibility and splitting property''. Journal of the Textile Institute, 108(12), 2154-2165.
Özet: In this paper, we focused on a real-life, large-scale problem of loom scheduling, which has 1100 independent jobs and 133 unrelated parallel machines with machine eligibility constraint. The authors focused on the scheduling problems that includes sequence-dependent setup times and the job splitting property to minimize the makespan. An adapted version of hybrid genetic algorithm in this study, incorporated a machine eligibility constraint. Adding this constraint complicated the problem, but doing so is compulsory to solve a real-life problem. To simplify the problem, we used the typesetting structure of the weaving industry. Utilizing random key numbers provided feasible chromosomes for each generation. Flexible chromosome structures and local search adaptation into the genetic algorithm were some of the other factors that allowed us to improve the makespan by up to 14% in this application.
URI: https://doi.org/10.1080/00405000.2017.1316177
https://www.tandfonline.com/doi/full/10.1080/00405000.2017.1316177
1754-2340
http://hdl.handle.net/11452/30981
ISSN: 0040-5000
Koleksiyonlarda Görünür:Scopus
Web of Science

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