APPLYING IMPROVED GENETIC ALGORITHM FOR SOLVING JOB SHOP SCHEDULING PROBLEMS (CROSBI ID 241794)
Prilog u časopisu | prethodno priopćenje
Podaci o odgovornosti
Janes, Gordan ; Perinic, Mladen ; Jurkovic, Zoran
engleski
APPLYING IMPROVED GENETIC ALGORITHM FOR SOLVING JOB SHOP SCHEDULING PROBLEMS
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.
Genetic algorithms ; scheduling ; optimization ; serial production
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Podaci o izdanju
24 (4)
2017.
1243-1247
objavljeno
1330-3651
1848-6339
10.17559/TV-20150527133957