In this paper, permutation flow shop scheduling problems (PFSS) are investigated with a genetic algorithm. PFSS problem is a special type of flow shop scheduling problem. In a PFSS problem, there are n jobs to be processed on m machines in series. Each job has to follow the same machine order and each machine must process jobs in the same job order. The most common performance criterion in the literature is the makespan for permutation scheduling problems. In this paper, a genetic algorithm is applied to minimize the makespan. Taillard’s instances including 20, 50, and 100 jobs with 5, 10, and 20 machines are used to define the efficiency of the proposed GA by considering lower bounds or optimal makespan values of instances. Furthermore, a sensitivity analysis is made for the parameters of the proposed GA and the sensitivity analysis shows that crossover probability does not affect solution quality and elapsed time. Supplementary to the parameter tuning of the proposed GA, we compare our GA with an existing GA in the literature for PFSS problems and our experimental study reveals that our proposed and well-tuned GA outperforms the existing GA for PFSS problems when the objective is to minimize the makespan.
Eser Adı (dc.title) | Genetic algorithm application for permutation flow shop scheduling problems |
Yazar (dc.contributor.author) | Oğuzhan Ahmet ARIK |
Tür (dc.type) | Makale/Derleme |
Dizin Platformu (dc.relation.platform) | WOS |
Tarih (dc.date.issued) | 2022 |
WOS Kategorileri (dc.identifier.wos) | SCI, SCI-Exp, SSCI, AHCI endekslerine giren dergilerde yayımlanan makaleler |
Makalenin Sayısı (dc.identifier.issue) | 1 |
Cilt Numarası (dc.identifier.volume) | 35 |
Yayıncı (dc.publisher) | Gazi University Journal of Science and Technology |
Yayının Son Sayfa Sayısı (dc.identifier.endpage) | 111 |
Yayının İlk Sayfa Sayısı (dc.identifier.startpage) | 92 |
DOI Numarası (dc.identifier.doi) | 10.35378/gujs.682388 |
ORCID No (dc.contributor.orcid) | 0000-0002-7088-2104 |
Dil (dc.language.iso) | EN |
Tam Metin Yayınlansın Mı? (dc.identifier.tammetin) | Evet |
Özet (dc.description.abstract) | In this paper, permutation flow shop scheduling problems (PFSS) are investigated with a genetic algorithm. PFSS problem is a special type of flow shop scheduling problem. In a PFSS problem, there are n jobs to be processed on m machines in series. Each job has to follow the same machine order and each machine must process jobs in the same job order. The most common performance criterion in the literature is the makespan for permutation scheduling problems. In this paper, a genetic algorithm is applied to minimize the makespan. Taillard’s instances including 20, 50, and 100 jobs with 5, 10, and 20 machines are used to define the efficiency of the proposed GA by considering lower bounds or optimal makespan values of instances. Furthermore, a sensitivity analysis is made for the parameters of the proposed GA and the sensitivity analysis shows that crossover probability does not affect solution quality and elapsed time. Supplementary to the parameter tuning of the proposed GA, we compare our GA with an existing GA in the literature for PFSS problems and our experimental study reveals that our proposed and well-tuned GA outperforms the existing GA for PFSS problems when the objective is to minimize the makespan. |
İsmi Geçen (dc.identifier.ismigecen) | Web Of Science ismi geçen |
Açık Erişim Tarihi (dc.date.available) | 2024-02-01 |
Konu Başlıkları (dc.subject) | Genetic algorithm |
Konu Başlıkları (dc.subject) | Permutation flow shop |
Konu Başlıkları (dc.subject) | Scheduling |
Konu Başlıkları (dc.subject) | Makespans |