Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지
DOI QR코드

DOI QR Code

Minimizing the Total Stretch in Flow Shop Scheduling with Limited Capacity Buffers

한정된 크기의 버퍼가 있는 흐름 공정 일정계획의 스트레치 최소화

  • Yoon, Suk-Hun (Department of Industrial and Information Systems Engineering, Soongsil University)
  • 윤석훈 (숭실대학교 산업정보시스템공학과)
  • Received : 2014.11.11
  • Accepted : 2014.12.01
  • Published : 2014.12.15
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, a hybrid genetic algorithm (HGA) approach is proposed for an n-job, m-machine flow shop scheduling problem with limited capacity buffers with blocking in which the objective is to minimize the total stretch. The stretch of a job is the ratio of the amount of time the job spent before its completion to its processing time. HGA adopts the idea of seed selection and development in order to improve the exploitation and exploration power of genetic algorithms (GAs). Extensive computational experiments have been conducted to compare the performance of HGA with that of GA.

Keywords

References

  1. Agnetis, A., Pacciarelli, D., and Rossi, F. (1997), Batch scheduling in a two-machine flow shop with limited buffer, Discrete Applied Mathematics, 72, 243-260. https://doi.org/10.1016/0166-218X(95)00114-7
  2. Bender, M. A., Muthukrishnan, S., and Rajaraman, R. (2004), Approximation algorithms for average stretch scheduling, Journal of Scheduling, 7, 195-222. https://doi.org/10.1023/B:JOSH.0000019681.52701.8b
  3. Bhattacharyya, S. (1999), Direct marketing performance modeling using genetic algorithms, INFORMS Journal on Computing, 11, 248-257. https://doi.org/10.1287/ijoc.11.3.248
  4. Chan, W.-T., Lan, T.-W., Liu, K.-S., and Wong, P. W. H. (2006), New resource augmentation analysis of the total stretch of SRPT and SJF in multiprocessor scheduling, Theoretical Computer Science, 359, 430-439. https://doi.org/10.1016/j.tcs.2006.06.003
  5. Dreo, J., Petrowski, A., Siarry, P., and Taillard, E. (2005), Metaheuristics for Hard Optimization : Methods and Case Studies, Springer, New York.
  6. Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.
  7. Lee, C.-Y., Piramuthu, S., and Tsai, Y.-K. (1997), Job shop scheduling with a genetic algorithm and machine learning, International Journal of Production Research, 35, 1171-1191. https://doi.org/10.1080/002075497195605
  8. Liepins, G. E. and Hilliard, M. R. (1989), Genetic algorithms : Foundation and applications, Annals of Operations Research, 21(1-4), 31-58. https://doi.org/10.1007/BF02022092
  9. Liu, B., Wang, L., and Jin, Y.-H. (1989), An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers, Computers and Operations Research, 35, 2791-2806.
  10. Muthukrishnan, S., Rajaraman, R., Shaheen, A., and Gehrke, J. F. (2005), Online scheduling to minimize average stretch, Siam Journal on Computing, 34(2), 433-452. https://doi.org/10.1137/S0097539701387544
  11. Norman, B. A. (1999), Scheduling flowshops with finite buffers and sequence-dependent setup times, Computers and Industrial Engineering, 36, 163-177. https://doi.org/10.1016/S0360-8352(99)00007-8
  12. Nowicki, E. (1999), The permutation flow shop with buffers : A tabu search approach, European Journal of Operational Research, 116, 205-219. https://doi.org/10.1016/S0377-2217(98)00017-4
  13. Pinedo, M. L. (2012), Scheduling : Theory, Algorithms, and Systems (4th Ed.), Springer, New York.
  14. Pranzo, M. (2004), Batch scheduling in a two-machine flow shop with limited buffer and sequence independent setup times and removal times, European Journal of Operational Research, 153, 581-592. https://doi.org/10.1016/S0377-2217(03)00264-9
  15. Quian, B., Wang, L., Huang, D. X., and Wang, X. (2009), An effective hybrid DE-based algorithm for flow shop scheduling with limited buffers, International Journal of Production Research, 47(1), 1-24. https://doi.org/10.1080/00207540701528750
  16. Smutnicki, C. (1998), A two-machine permutation flow shop scheduling problem with buffers, OR Spektrum, 20, 229-235. https://doi.org/10.1007/BF01539740
  17. Wang, X. and Tang, L. (2009), A tabu search heuristic for the hybrid flowshop scheduling with finite intermediate buffers, Computers and Operations Research, 36, 907-918. https://doi.org/10.1016/j.cor.2007.11.004
  18. Wardono, B. and Fathi, Y. (2004), A tabu search algorithm for the multi-stage parallel machine problem with limited buffer capacities, European Journal of Operational Research, 155, 380-401. https://doi.org/10.1016/S0377-2217(02)00873-1