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- ISSN: 2010-0248 (Print)
- Abbreviated Title: Int. J. Innov. Manag. Technol.
- Frequency: Quarterly
- DOI: 10.18178/IJIMT
- Editor-in-Chief: Prof. Jin Wang
- Managing Editor: Ms. Nancy Y. Liu
- Abstracting/ Indexing: Google Scholar, CNKI, Ulrich's Periodicals Directory, Crossref, Electronic Journals Library.
E-mail: editor@ijimt.org
- Article Processing Charge: 500 USD
Genetic Algorithm for Job Shop Scheduling Problem: A Case Study
Abstract—The job-shop scheduling (JSS) is a schedule planning for low volume systems with many variations in requirements. In job-shop scheduling problem (JSSP), there are k operations and n jobs to be processed on m machines with a certain objective function to be minimized. Due to complexity of transferring work in process product, this research add transfer time variable from one machine to another for each different operation. Performance measures are mean flow time and make span. In this paper we used genetic algorithm (GA) with some modifications to deal with problem of job shop scheduling. The result than is compared with dispatching rules such as longest processing time, shortest processing time and first come first serve. The numerical example showed that GA result can outperform the other three methods.
Index Terms—Job shop, scheduling, genetic algorithm,dispatching rules.
Meilinda F. N. Maghfiroh and Vincent F. Yu are with the National Taiwan University of Science and Technology, Taiwan ROC (e-mail:meilinda.maghfiroh@gmail.com, vincent@mail.ntust.edu.tw).
Agus Darmawan is with Gadjah Mada University, Indonesia (e-mail:agusdrmwn@gmail.com).t>
Cite: Meilinda F. N. Maghfiroh, Agus Darmawan, and Vincent F. Yu, "Genetic Algorithm for Job Shop Scheduling Problem: A Case Study," International Journal of Innovation, Management and Technology vol. 4, no. 1, pp. 137-140, 2013.