DOI QR코드

DOI QR Code

A Genetic Algorithm and Discrete-Event Simulation Approach to the Dynamic Scheduling

유전 알고리즘과 시뮬레이션을 통한 동적 스케줄링

  • Yoon, Sanghan (Daegyeong Institute for Regional Program Evaluation) ;
  • Lee, Jonghwan (School of Industrial Engineering, Kumoh National Institute of Technology) ;
  • Jung, Gwan-Young (School of Industrial Engineering, Kumoh National Institute of Technology) ;
  • Lee, Hyunsoo (School of Industrial Engineering, Kumoh National Institute of Technology) ;
  • Wie, Doyeong (School of Industrial Engineering, Kumoh National Institute of Technology) ;
  • Jeong, Jiyong (School of Industrial Engineering, Kumoh National Institute of Technology) ;
  • Seo, Yeongbok (School of Industrial Engineering, Kumoh National Institute of Technology)
  • 윤상한 (대경지역사업평가원) ;
  • 이종환 (금오공과대학교 산업공학부) ;
  • 정관영 (금오공과대학교 산업공학부) ;
  • 이현수 (금오공과대학교 산업공학부) ;
  • 위도영 (금오공과대학교 산업공학부) ;
  • 정지용 (금오공과대학교 산업공학부) ;
  • 서영복 (금오공과대학교 산업공학부)
  • Received : 2013.12.05
  • Accepted : 2013.12.13
  • Published : 2013.12.31

Abstract

This study develops a dynamic scheduling model for parallel machine scheduling problem based on genetic algorithm (GA). GA combined with discrete event simulation to minimize the makespan and verifies the effectiveness of the developed model. This research consists of two stages. In the first stage, work sequence will be generated using GA, and the second stage developed work schedule applied to a real work area to verify that it could be executed in real work environment and remove the overlapping work, which causes bottleneck and long lead time. If not, go back to the first stage and develop another schedule until satisfied. Small size problem was experimented and suggested a reasonable schedule within limited resources. As a result of this research, work efficiency is increased, cycle time is decreased, and due date is satisfied within existed resources.

Keywords

References

  1. Hong Zhou, Yuncheng Feng, and Limin Han, The hybrid heuristic genetic algorithm for job ship scheduling. Computers and Industrial Engineering, 2008, Vol. 40, p 191- 200.
  2. Pezzella, F., Morganti, G., and Ciaschetti, G., A genetic algorithm for the Flexible Jon-shop Scheduling Problem. Computers and Operations Research, 2008, Vol. 35, p 3202-3212. https://doi.org/10.1016/j.cor.2007.02.014
  3. Jose Fernando Goncalves, Jorge Jose Magalhaes Medes, and Mauricio G.C. Resende, A hybrid genetic algorithm for the job shop scheduling problem. European Journal of Operational Research, 2005, p. 77-95.
  4. Birkan Can, and Cathal Heavey, Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems. Computers and Industrial Engineering, 2011, Vol. 61, p 447-462. https://doi.org/10.1016/j.cie.2011.03.012
  5. Savas, B., Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation. Information Sciences, 2011, Vol. 181, p 3551-3569. https://doi.org/10.1016/j.ins.2011.04.010
  6. Hong Zhou, Waiman Cheung, and Lawrence C. Leung, Minimizing weighted tardiness of job-shop scheduling using a hybrid genetic algorithm. European Journal of Operational Research, 2009, Vol. 194, p 637-649. https://doi.org/10.1016/j.ejor.2007.10.063
  7. Catherine Azzaro-Pantel, Leonardo Bernal-Haro, Philippe Baudet, Serge Domenech, and Luc Pibouleau, A twostage methodology for short-term batch plant scheduling : discrete-event simulation and genetic algorithm, 1998, Vol. 22, No. 10, p 1461-1481. https://doi.org/10.1016/S0098-1354(98)80033-1

Cited by

  1. 유전 알고리즘을 이용한 Work-In-Process 수준 최적화 vol.40, pp.1, 2013, https://doi.org/10.11627/jkise.2017.40.1.079