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Journal of Scheduling

, Volume 15, Issue 4, pp 487–497 | Cite as

Quantity-based buffer-constrained two-machine flowshop problem: active and passive prefetch models for multimedia applications

  • Alexander KononovEmail author
  • Jen-Shin Hong
  • Polina Kononova
  • Feng-Cheng Lin
Article

Abstract

Conventional studies on buffer-constrained flowshop scheduling problems have considered applications with a limitation on the number of jobs that are allowed in the intermediate storage buffer before flowing to the next machine. The study in Lin et al. (Comput. Oper. Res. 36(4):1158–1175, 2008a) considered a two-machine flowshop problem with “processing time-dependent” buffer constraints for multimedia applications. A “passive” prefetch model (the PP-problem), in which the download process is suspended unless the buffer is sufficient for keeping an incoming media object, was applied in Lin et al. (Comput. Oper. Res. 36(4):1158–1175, 2008a). This study further considers an “active” prefetch model (the AP-problem) that exploits the unoccupied buffer space by advancing the download of the incoming object by a computed maximal duration that possibly does not cause a buffer overflow. We obtain new complexity results for both problems.

This study also proposes a new lower bound which improves the branch and bound algorithm presented in Lin et al. (Comput. Oper. Res. 36(4):1158–1175, 2008a). For the PP-problem, compared to the lower bounds developed in Lin et al. (Comput. Oper. Res. 36(4):1158–1175, 2008a), on average, the results of the simulation experiments show that the proposed new lower bound cuts about 38% of the nodes and 32% of the execution time for searching the optimal solutions.

Keywords

Scheduling Flowshop Buffer Multimedia applications 

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References

  1. Brucker, P., & Knust, S. (2006). Complex scheduling. Berlin: Springer. Google Scholar
  2. Gilmore, P. C., & Gomory, R. E. (1964). Sequencing a one state-variable machine: a solvable case of the traveling salesman problem. Operations Research, 12(5), 655–679. CrossRefGoogle Scholar
  3. Garey, M. R., & Johnson, D. S. (1979). Computer and intractability: a guide to the theory of NP-completeness. San Francisco: Freeman. Google Scholar
  4. Hong, J.-S., Chen, B.-S., Hung, S.-H., & Hsiang, J. (2005). Toward an integrated digital museum system—the Chi Nan experiences. International Journal on Digital Libraries, 5(3), 231–251. CrossRefGoogle Scholar
  5. Johnson, S. M. (1954). Optimal two- and three-stage production scheduling with setup time included. Naval Research Logistics Quarterly, 1, 61–68. CrossRefGoogle Scholar
  6. Korte, B., & Vygen, J. (2000). Combinatorial optimization: theory and algorithms. Algorithms and combinatorics: Vol. 21. Berlin: Springer. Google Scholar
  7. Levner, E. V. (1973). On a network approach to solving scheduling problems. In Ya. Z. Zipkin (Ed.), Modern problems of control theory (pp. 43–54). Moscow: Institute of Control Problems Press (in Russian) Google Scholar
  8. Lin, F.-C., Hong, J.-S., & Lin, B. M. T. (2008a). A two-machine flow shop problem with processing time-dependent buffer constraints—an application in multimedia problem. Computers & Operations Research, 36(4), 1158–1175. CrossRefGoogle Scholar
  9. Lin, F.-C., Lai, C.-Y., & Hong, J.-S. (2008b). Minimize presentation lag by sequencing media objects for auto-assembled presentations from digital libraries. Data & Knowledge Engineering, 66(3), 384–401. CrossRefGoogle Scholar
  10. Lin, F.-C., Lai, C.-Y., & Hong, J.-S. (2009). Heuristic algorithms for ordering media objects to reduce presentation lags in auto-assembled multimedia presentations from digital libraries. Electronic Library, 27(1), 134–148. CrossRefGoogle Scholar
  11. Papadimitriou, C. H., & Kanellakis, P. C. (1980). Flowshop scheduling with limited temporary storage. Journal of the Association for Computing Machinery, 27(3), 533–549. CrossRefGoogle Scholar
  12. Witt, A., & Voß, S. (2007). Simple heuristics for scheduling with limited intermediate storage. Computers & Operations Research, 34(8), 2293–2309. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Alexander Kononov
    • 1
    Email author
  • Jen-Shin Hong
    • 2
  • Polina Kononova
    • 1
  • Feng-Cheng Lin
    • 3
  1. 1.Sobolev Institute of MathematicsSiberian Branch of the Russian Academy of SciencesNovosibirskRussia
  2. 2.Dept. of Computer Science & Information EngineeringNational Chi Nan UniversityPuliTaiwan
  3. 3.Institute for Information IndustryTaipeiTaiwan

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