Log in

ACO-based multi-objective scheduling of parallel batch processing machines with advanced process control constraints

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This research was motivated by a scheduling problem in the dry strip operations of a semiconductor wafer fabrication facility. The machines were modeled as parallel batch processing machines with incompatible job families and dynamic job arrivals, and constraints on the sequence-dependent setup time and the qual-run requirements of advanced process control. The optimization had multiple objectives, the total weighted tardiness (TWT) and makespan, to consider simultaneously. Since the problem is NP-hard, we used an Ant Colony Optimization (ACO) algorithm to achieve a satisfactory solution in a reasonable computation time. A variety of simulation experiments were run to choose ACO parameter values and to demonstrate the performance of the proposed method. The simulation results showed that the proposed ACO algorithm is superior to the common Apparent Tardiness Cost-Batched Apparent Tardiness Cost rule for minimizing the TWT and makespan. The arrival time distribution and the number of jobs strongly affected the ACO algorithm’s performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Mathirajan M, Sivakumar AI (2006) A literature review, classification and simple meta-analysis on scheduling of batch processors in semiconductor. Int J Adv Manuf Technol 29(9–10):990–1001. doi:10.1007/s00170-005-2585-1

    Article  Google Scholar 

  2. Chien C-F, Chen C-H (2007) A novel timetabling algorithm for a furnace process for semiconductor fabrication with constrained waiting and frequency-based setups. OR-Spectrum 29:391–419. doi:10.1007/s00291-006-0062-3

    Article  MATH  Google Scholar 

  3. Chou F-D, Chang P-C, Wang H-M (2006) A hybrid genetic algorithm to minimize makespan for the single batch machine dynamic scheduling problem. Int J Adv Manuf Technol 31(3–4):350–359. doi:10.1007/s00170-005-0194-7

    Article  Google Scholar 

  4. Gupta A-K, Sivakumar A-I (2007) Controlling delivery performance in semiconductor manufacturing using Look Ahead Batching. Int J Prod Res 45(3):591–613. doi:10.1080/00207540600792226

    Article  MATH  Google Scholar 

  5. Erramilli V, Mason S-J (2006) Multiple orders per job compatible batch scheduling. IEEE Trans Electron Packag Manuf 29(4):285–296. doi:10.1109/TEPM.2006.887355

    Article  Google Scholar 

  6. Solomon M, Fowler J-W, Pfund M et al (2002) The inclusion of future arrivals and downstream setups into wafer fabrication batch processing decisions. J Electron Manuf 11(2):149–159. doi:10.1142/S0960313102000370

    Article  Google Scholar 

  7. Mönch L, Zimmermann J, Otto P (2006) Machine learning techniques for scheduling jobs with incompatible families and unequal ready times on parallel batch machines. Eng Appl Artif Intell 19(3):235–245. doi:10.1016/j.engappai.2005.10.001

    Article  Google Scholar 

  8. Liu L-L, Ng C-T, Cheng T-C-E (2007) Scheduling jobs with agreeable processing times and due dates on a single batch processing machine. Theor Comput Sci 374:159–169. doi:10.1016/j.tcs.2006.12.039

    Article  MATH  MathSciNet  Google Scholar 

  9. Dorigo M (1992) Ottimizzazione, Apprendimento Automatico, ed Algoritmi Basati su Metafora Naturale (Optimization, Learning and Natural Algorithms). Ph.D. thesis, Politecnico di Milano, Italy (in Italian), pp 140

  10. Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press/Bradford Books, Cambridge

    MATH  Google Scholar 

  11. Srinivasa Raghavan N-R, Venkataramana M (2006) Scheduling parallel batch processors with incompatible job families using ant colony optimization. Proceedings of the 2006 IEEE International Conference on Automation Science and Engineering, Shanghai, China, Oct 8–10 2006:507–512

  12. Balasubramanian H, Mönch L, Fowler J et al (2004) Genetic algorithm based scheduling of parallel batch machines with incompatible job families to minimize total weighted tardiness. Int J Prod Res 42(8):1621–1638. doi:10.1080/00207540310001636994

    Article  MATH  Google Scholar 

  13. Cai Y-W, Kutanoglu E, Hesenbein J et al (2007) Single-machine scheduling problem with advanced process control constraints. AEC/APC Symposium XIX, Indian Wells, CA, USA

  14. Patel N-S (2004) Lot allocation and process control in semiconductor manufacturing—a dynamic game approach. Proceedings of 43rd IEEE Conference on Decision and Control, Volume 4, Nassau, Bahamas, Dec 14–17 2004: 4243–4248

  15. Mönch L, Balasubramanian H, Fowler J-W et al (2005) Heuristic scheduling of jobs on parallel batch machines with incompatible job families and unequal ready times. Comput Oper Res 32(11):2731–2750. doi:10.1016/j.cor.2004.04.001

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Li Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, L., Qiao, F. & Wu, Q.D. ACO-based multi-objective scheduling of parallel batch processing machines with advanced process control constraints. Int J Adv Manuf Technol 44, 985–994 (2009). https://doi.org/10.1007/s00170-008-1904-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-008-1904-8

Keywords

Navigation