Tehnički vjesnik, Vol. 24 No. 4, 2017.
Prethodno priopćenje
https://doi.org/10.17559/TV-20150527133957
An efficient genetic algorithm for job shop scheduling problems
Gordan Janes
orcid.org/0000-0002-5654-351X
; Center for advanced computing and modelling, University of Rijeka, Radmile Matejčić 2, HR-51000 Rijeka, Croatia
Mladen Perinic
orcid.org/0000-0003-3028-8466
; Department of Industrial Engineering and Management, Faculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, Croatia
Zoran Jurkovic
orcid.org/0000-0002-7202-156X
; Department of Industrial Engineering and Management, Faculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, Croatia
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.
Ključne riječi
genetic algorithms; optimization; scheduling; serial production
Hrčak ID:
185512
URI
Datum izdavanja:
31.7.2017.
Posjeta: 4.651 *