Pregled bibliografske jedinice broj: 889877
APPLYING IMPROVED GENETIC ALGORITHM FOR SOLVING JOB SHOP SCHEDULING PROBLEMS
APPLYING IMPROVED GENETIC ALGORITHM FOR SOLVING JOB SHOP SCHEDULING PROBLEMS // Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku, 24 (2017), 4; 1243-1247 doi:10.17559/TV-20150527133957 (podatak o recenziji nije dostupan, prethodno priopćenje, znanstveni)
CROSBI ID: 889877 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
APPLYING IMPROVED GENETIC ALGORITHM FOR SOLVING JOB SHOP SCHEDULING PROBLEMS
Autori
Janes, Gordan ; Perinic, Mladen ; Jurkovic, Zoran
Izvornik
Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku (1330-3651) 24
(2017), 4;
1243-1247
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, prethodno priopćenje, znanstveni
Ključne riječi
Genetic algorithms ; scheduling ; optimization ; serial production
Sažetak
The Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling combinatorial problems with considerable importance in industry. When solving complex problems, search based on traditional genetic algorithms has a major drawback - high requirement for computational power. The goal of this research was to develop fast and efficient scheduling method based on genetic algorithm for solving the job-shop scheduling problems. In proposed GA initial population is generated randomly, and the relevant crossover and mutation operation is also designed. This paper presents an efficient genetic algorithm for solving job-shop scheduling problems. Performance of the algorithm is demonstrated in the real-world examples.
Izvorni jezik
Engleski
Znanstvena područja
Strojarstvo
POVEZANOST RADA
Ustanove:
Tehnički fakultet, Rijeka
Citiraj ovu publikaciju:
Časopis indeksira:
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus
