Skip to main content

A PTAS for Minimizing Weighted Completion Time on Uniformly Related Machines

Extended Abstract

  • Conference paper
  • First Online:
Automata, Languages and Programming (ICALP 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2076))

Included in the following conference series:

Abstract

We consider the well known problem of scheduling jobs with release dates to minimize their average weighted completion time. When multiple machines are available, the machine environment may range from identical machines (the processing time required by a job is invariant across the machines) at one end of the spectrum to unrelated machines (the processing time required by a job on each machine is specified by an arbitrary vector) at the other end. While the problem is strongly NP-hard even in the case of a single machine, constant factor approximation algorithms are known for even the most general machine environment of unrelated machines. Recently a PTAS was discovered for the case of identical parallel machines [1]. In contrast, the problem is MAX SNP-hard for unrelated machines [11]. An important open problem was to determine the approximability of the intermediate case of uniformly related machines where each machine has a speed and it takes p=s time to process a job of size p on a machine with speed s. We resolve the complexity of this problem by obtaining a PTAS. This improves the earlier known approximation ratio of (2 + ∈).

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. F. Afrati, E. Bampis, C. Chekuri, D. Karger, C. Kenyon, S. Khanna, I. Milis, M. Queyranne, M. Skutella, C. Stein, and M. Sviridenko. Approximation Schemes for Minimizing Average Weighted Completion Time with Release Dates. FOCS’ 99.

    Google Scholar 

  2. N. Alon, Y. Azar, G. J. Woeginger, and T. Yadid. Approximation schemes for scheduling on parallel machines. Journal of Scheduling, 1:55–66, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  3. S. Chakrabarti, C. A. Phillips, A. S. Schulz, D. B. Shmoys, C. Stein, and J. Wein. Improved scheduling algorithms for minsum criteria. ICALP’ 96.

    Google Scholar 

  4. C. Chekuri and S. Khanna. A PTAS for the Multiple Knapsack Problem. SODA’ 00.

    Google Scholar 

  5. C. Chekuri, R. Motwani, B. Natarajan, and C. Stein. Approximation techniques for average completion time scheduling. SODA’ 97.

    Google Scholar 

  6. M. X. Goemans. Improved approximation algorithms for scheduling with release dates. SODA’ 97.

    Google Scholar 

  7. R. L. Graham, E. L. Lawler, J. K. Lenstra, and A. H. G Rinnooy Kan. Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Discrete Math., 5:287–326, 1979.

    Article  MATH  MathSciNet  Google Scholar 

  8. L. A. Hall, A. S. Schulz, D. B. Shmoys, and J. Wein. Scheduling to minimize average completion time: Offline and online algorithms. Math. of OR, 513-44,’ 97.

    Google Scholar 

  9. D. S. Hochbaum and D. B. Shmoys. Using dual approximation algorithms for scheduling problems: theoretical and practical results. JACM, 34:144–162, 1987.

    Article  MathSciNet  Google Scholar 

  10. D. S. Hochbaum and D. B. Shmoys. A polynomial approximation scheme for scheduling on uniform processors: using the dual approximation approach. SIAM Journal on Computing, 17:539–551, 1988.

    Article  MATH  MathSciNet  Google Scholar 

  11. J. A. Hoogeveen, P. Schuurman, and G. J. Woeginger. Non-approximability results for scheduling problems with minsum criteria. IPCO’ 98.

    Google Scholar 

  12. J. K. Lenstra, A. H. G. Rinnooy Kan, and P. Brucker. Complexity of machine scheduling problems. Annals of Discrete Mathematics, 1:343–362, 1977.

    Article  MathSciNet  Google Scholar 

  13. A. Munier, M. Queyranne, and A. S. Schulz. Approximation bounds for a general class of precedence constrained parallel machine scheduling problems. IPCO’ 98.

    Google Scholar 

  14. C. Phillips, C. Stein, and J. Wein. Minimizing average completion time in the presence of release dates. Mathematical Programming B, 82:199–223, 1998.

    MathSciNet  Google Scholar 

  15. A. S. Schulz and M. Skutella. Scheduling-LPs bear probabilities: Randomized approximations for min-sum criteria. ESA’ 97.

    Google Scholar 

  16. M. Skutella and G. J. Woeginger. A PTAS for minimizing the weighted sum of job completion times on parallel machines. STOC’ 99.

    Google Scholar 

  17. W. E. Smith. Various optimizers for single-stage production. Naval Res. Logist. Quart., 3:59–66, 1956.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chekuri, C., Khanna, S. (2001). A PTAS for Minimizing Weighted Completion Time on Uniformly Related Machines. In: Orejas, F., Spirakis, P.G., van Leeuwen, J. (eds) Automata, Languages and Programming. ICALP 2001. Lecture Notes in Computer Science, vol 2076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48224-5_69

Download citation

  • DOI: https://doi.org/10.1007/3-540-48224-5_69

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42287-7

  • Online ISBN: 978-3-540-48224-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics