doi: 10.7763/IJIMT.2013.V4.376
Genetic Algorithm for Job Shop Scheduling Problem: A Case Study
Meilinda F. N. Maghfiroh1
,
Agus Darmawan2 ,
Vincent F. Yu1
- 1National Taiwan University of Science and Technology, Taiwan ROC.
- 2Gadjah Mada University, Indonesia.
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.
Keywords
- Job shop
- scheduling
- genetic algorithm
- dispatching rules
How to 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. https://doi.org/10.7763/IJIMT.2013.V4.376
Copyright & License
Copyright © 2013 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).