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http://hdl.handle.net/11452/28355
Başlık: | Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times |
Yazarlar: | Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü. Yılmaz, Duygu Eroğlu Özmutlu, Hüseyin Cenk Özmutlu, Seda AAH-1079-2021 AAH-4480-2021 ABH-5209-2020 56120864000 6603061328 6603660605 |
Anahtar kelimeler: | Parallel machine scheduling Sequence-dependent set-up times Genetic algorithms Minimize Jobs Makespan Engineering Operations research & management science Genes Machinery Random number generation Scheduling algorithms Chromosome structure Completion time Computational results Local search operation Search algorithms Sequence-dependent set-up time Unrelated parallel machines |
Yayın Tarihi: | 2014 |
Yayıncı: | Taylor & Francis |
Atıf: | Yılmaz, D. E. vd. (2014). "Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times". International Journal of Production Research, 52(19), 5841-5856. |
Özet: | In this paper, a genetic algorithm (GA) with local search is proposed for the unrelated parallel machine scheduling problem with the objective of minimising the maximum completion time (makespan). We propose a simple chromosome structure consisting of random key numbers in a hybrid genetic-local search algorithm. Random key numbers are frequently used in GAs but create additional difficulties when hybrid factors are implemented in a local search. The best chromosome of each generation is improved using a local search during the algorithm, but the better job sequence (which might appear during the local search operation) must be adapted to the chromosome that will be used in each successive generation. Determining the genes (and the data in the genes) that would be exchanged is the challenge of using random numbers. We have developed an algorithm that satisfies the adaptation of local search results into the GAs with a minimum relocation operation of the genes' random key numbers - this is the main contribution of the paper. A new hybrid approach is tested on a set of problems taken from the literature, and the computational results validate the effectiveness of the proposed algorithm. |
URI: | https://doi.org/10.1080/00207543.2014.920966 https://www.tandfonline.com/doi/full/10.1080/00207543.2014.920966 http://hdl.handle.net/11452/28355 |
ISSN: | 0020-7543 1366-588X |
Koleksiyonlarda Görünür: | TrDizin Web of Science |
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