Preliminary communication

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

Abstract

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.

Keywords

genetic algorithms; optimization; scheduling; serial production

Hrčak ID:

185512

URI

https://hrcak.srce.hr/185512

Publication date:

31.7.2017.

Article data in other languages: croatian

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