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 Bibliografske baze podataka

Pregled bibliografske jedinice broj: 889877

Časopis

Autori: Janes, Gordan; Perinic, Mladen; Jurkovic, Zoran
Naslov: APPLYING IMPROVED GENETIC ALGORITHM FOR SOLVING JOB SHOP SCHEDULING PROBLEMS
( APPLYING IMPROVED GENETIC ALGORITHM FOR SOLVING JOB SHOP SCHEDULING PROBLEMS )
Izvornik: Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku (1330-3651) 24 (2017), 4; 1243-1247
Vrsta rada: prethodno priopćenje
Ključne riječi: Genetic algorithms ; scheduling ; optimization ; serial production
( 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: eng
Rad je indeksiran u
bazama podataka:
Scopus
SCI-EXP, SSCI i/ili A&HCI
Science Citation Index Expanded (SCI-EXP) (sastavni dio Web of Science Core Collectiona)
Kategorija: Znanstveni
Znanstvena područja:
Strojarstvo
URL Internet adrese: https://hrcak.srce.hr/file/273552
Broj citata:
Altmetric:
DOI: 10.17559/TV-20150527133957
URL cjelovitog teksta: https://hrcak.srce.hr/file/273552
Google Scholar: APPLYING IMPROVED GENETIC ALGORITHM FOR SOLVING JOB SHOP SCHEDULING PROBLEMS
Upisao u CROSBI: Mladen Perinić (mladen.perinic@riteh.hr), 1. Ruj. 2017. u 11:57 sati



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