Skip to main content

Polynomial Approximation Schemes for the Max-Min Allocation Problem under a Grade of Service Provision

  • Conference paper
  • 1200 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5573))

Abstract

The max-min allocation problem under a grade of service provision is defined in the following model: given a set \({\cal M}\) of m parallel machines and a set \({\cal J}\) of n jobs, where machines and jobs are all entitled to different levels of grade of service (GoS), each job \(J_j\in {\cal J}\) has its processing time p j and it is only allocated to a machine M i whose GoS level is no more than the GoS level the job J j has. The goal is to allocate all jobs to m machines to maximize the minimum machine load, where the machine load of machine M i is the sum of the precessing times of jobs executed on M i . The best approximation algorithm [4] to solve this problem produces an allocation in which the minimum machine completion time is at least Ω(logloglogm/loglogm) of the optimal value.

In this paper, we respectively present four approximation schemes to solve this problem and its two special versions: (1) a polynomial time approximation scheme (PTAS) with running time \(O(mn^{O(1/\epsilon^2)})\) for the general version, where ε> 0; (2) a PTAS and an fully polynomial time approximation scheme (FPTAS) with running time O(n) for the version where the number m of machines is fixed; (3) a PTAS with running time O(n) for the version where the number of GoS levels is bounded by k.

The work is fully supported by the National Natural Science Foundation of China [No.10861012,10561009], Natural Science Foundation of Yunnan Province [No.2006F0016M] and Foundation of Younger Scholar in Science and Technology of Yunnan Province [No.2007PY01-21].

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   109.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   139.00
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alon, N., Azar, Y., Woeginger, G.J., Yadid, T.: Approximation Schemes for Scheduling on Parallel Machines. Journal of Scheduling 1, 55–66 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  2. Asadpour, A., Feige, U., Saberi, A.: Santa Claus Meets Hypergraph Matchings. In: Goel, A., Jansen, K., Rolim, J.D.P., Rubinfeld, R. (eds.) APPROX and RANDOM 2008. LNCS, vol. 5171, pp. 10–20. Springer, Heidelberg (2008)

    Google Scholar 

  3. Asadpour, A., Saberi, A.: An Approximation Algorithm for Max-Min Fair Allocation of Indivisible Goods. In: Proc. of ACM-SIAM Symposium on the Theory of Computation (STOC), pp. 114–121 (2007)

    Google Scholar 

  4. Bansal, N., Sviridenko, M.: The Santa Claus Problem. In: Proc. of ACM-SIAM Symposium on the Theory of Computation (STOC), pp. 31–40 (2006)

    Google Scholar 

  5. Bezakova, I., Dani, V.: Allocating Indivisible Goods. SIGecom Exchanges 5(3), 11–18 (2005)

    Article  Google Scholar 

  6. Chakrabarty, D., Chuzhoy, J., Khanna, S.: On Allocating Goods to Maximize Fairness (manuscript, 2009)

    Google Scholar 

  7. Epstein, L., Sgall, J.: Approximation Schemes for Scheduling on Uniformly Related and Identical Parallel Machines. Algorithmica 39(1), 43–57 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  8. Feige, U.: On Allocations that Maximize Fairness. In: ACM-SIAM Annual Symposium on Discrete Algorithms (SODA), pp. 287–293 (2008)

    Google Scholar 

  9. Graham, R.L., Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G.: Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey. Annals of Discrete Mathematics 5, 287–326 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  10. Hwang, H.C., Chang, S.Y., Lee, K.: Parallel Machine Scheduling under a Grade of Service Provision. Computers and Operations Research 31, 2055–2061 (2004)

    Article  MATH  Google Scholar 

  11. Lenstra, H.W.: Integer Programming with a Fixed Number of Variables. Mathematics of Operations Research 8, 538–548 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  12. Lenstra, J.K., Shmoys, D.B., Tardos, E.: Approximation Algorithms for Scheduling Unrelated Parallel Machines. Mathematical Programming, Series A 46(2), 259–271 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  13. Ou, J., Leung, J.Y.-T., Li, C.L.: Scheduling Parallel Machines with Inclusive Processing Set Restrictions. Naval Research Logistics 55(4), 328–338 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  14. Schuurman, P., Woeginger, G.J.: Polynomial Time Approximation Algorithms for Machine Scheduling: Ten Open Problems. Journal of Scheduling 2, 203–213 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  15. Woeginger, G.J.: When does a Dynamic Programming Formulation Guarantee the Existence of a Fully Polynomial Time Approximation Scheme (FPTAS)? INFORMS Journal on Computing 12, 57–75 (2000)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, J., Li, W., Li, J. (2009). Polynomial Approximation Schemes for the Max-Min Allocation Problem under a Grade of Service Provision. In: Du, DZ., Hu, X., Pardalos, P.M. (eds) Combinatorial Optimization and Applications. COCOA 2009. Lecture Notes in Computer Science, vol 5573. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02026-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02026-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02025-4

  • Online ISBN: 978-3-642-02026-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics