This paper investigates parallel machine scheduling problems where the objectives are to minimize total completion times under effects of learning and deterioration. The investigated problem is in NP-hard class and solution time for finding optimal solution is extremely high. The authors suggested a genetic algorithm, a well-known and strong metaheuristic algorithm, for the problem and we generated some test problems with learning and deterioration effects. The proposed genetic algorithm is compared with another existing metaheuristic for the problem. Experimental results show that the proposed genetic algorithm yield good solutions in very short execution times and outperforms the existing metaheuristic for the problem.
Eser Adı (dc.title) | A Genetic Algorithm Approach To Parallel Machine Scheduling Problems Under Effects Of Position-Dependent Learning And Linear Deterioration |
Yazar (dc.contributor.author) | Oğuzhan Ahmet ARIK |
Tür (dc.type) | Makale/Derleme |
Dizin Platformu (dc.relation.platform) | WOS |
Tarih (dc.date.issued) | 2021 |
WOS Kategorileri (dc.identifier.wos) | SCI, SCI-Exp, SSCI, AHCI endekslerine giren dergilerde yayımlanan makaleler |
Makalenin Sayısı (dc.identifier.issue) | 3 |
Cilt Numarası (dc.identifier.volume) | 12 |
Yayıncı (dc.publisher) | International Journal of Applied Metaheuristic Computing |
Yayının İlk Sayfa Sayısı (dc.identifier.startpage) | 195-211 |
DOI Numarası (dc.identifier.doi) | DOI: 10.4018/IJAMC.2021070109 |
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) | This paper investigates parallel machine scheduling problems where the objectives are to minimize total completion times under effects of learning and deterioration. The investigated problem is in NP-hard class and solution time for finding optimal solution is extremely high. The authors suggested a genetic algorithm, a well-known and strong metaheuristic algorithm, for the problem and we generated some test problems with learning and deterioration effects. The proposed genetic algorithm is compared with another existing metaheuristic for the problem. Experimental results show that the proposed genetic algorithm yield good solutions in very short execution times and outperforms the existing metaheuristic for the problem. |
İsmi Geçen (dc.identifier.ismigecen) | Web Of Science ismi geçen |
Açık Erişim Tarihi (dc.date.available) | 2024-02-01 |
Konu Başlıkları (dc.subject) | Deterioration Effect |
Konu Başlıkları (dc.subject) | Genetic Algorithm |
Konu Başlıkları (dc.subject) | Learning Effect |
Konu Başlıkları (dc.subject) | Parallel Machine |
Konu Başlıkları (dc.subject) | EARLINESS/TARDINESS COSTS |
Konu Başlıkları (dc.subject) | JOB DETERIORATION |
Konu Başlıkları (dc.subject) | TIME |
Konu Başlıkları (dc.subject) | EARLINESS |
Konu Başlıkları (dc.subject) | MAKESPAN |
Konu Başlıkları (dc.subject) | MINIMIZE |