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Pregled bibliografske jedinice broj: 889877

APPLYING IMPROVED GENETIC ALGORITHM FOR SOLVING JOB SHOP SCHEDULING PROBLEMS


Janes, Gordan; Perinic, Mladen; Jurkovic, Zoran
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 priopcenje, znanstveni)


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 priopcenje, 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

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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