[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

A Coupled Gradient Network Approach for the Multi Machine Earliness and Tardiness Scheduling Problem

  • Conference paper
Computational Science and Its Applications – ICCSA 2005 (ICCSA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3483))

Included in the following conference series:

  • 1668 Accesses

Abstract

This paper considers the earliness and tardiness problem of sequencing a set of independent jobs on non-identical multi-machines, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. A coupled gradient network approach is employed to provide a shop scheduling analysis framework. The methodology is based on a penalty function approach used to construct the appropriate energy function and a gradient type network. The mathematical formulation of the problem is firstly presented and six coupled gradient networks are constructed to model the mixed nature of the problem. After the network architecture and the energy function were specified, the dynamics are defined by steepest gradient descent algorithm.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 88.00
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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.

Similar content being viewed by others

References

  1. Ahmed, M.U., Sundararaghavan, P.S.: Minimizing the weighted sum of late and early completion penalties in a single machine. IIE Transactions 22, 288–290 (1990)

    Article  Google Scholar 

  2. Rachavachari, M.: Scheduling problems with non-regular penalty functions - a review. Opsearch 25, 144–164 (1988)

    MathSciNet  Google Scholar 

  3. Baker, K.R., Scudder, G.D.: Sequencing with earliness and tardiness penalties: a review. Operations Research 38, 22–36 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  4. Arkin, E., Roundy, R.O.: Weighted-tardiness scheduling on parallel machines with proportional weights. Operations Research 39, 64–81 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  5. De, P., Ghosh, J.B., Wells, C.E.: Due dates and early/tardy scheduling on identical parallel machines. Naval Research Logistics 41, 17–32 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  6. Sundararaghavan, P., Ahmed, M.U.: Minimizing the sum of absolute lateness in single-machine and multimachine scheduling. Naval Research Logistics Quarterly 31, 25–33 (1984)

    Article  Google Scholar 

  7. Zhu, Z., Heady, R.: Minimizing the Sum of Job Earliness and Tardiness in a Multimachine System. International Journal of Production Research 36, 1619–1632 (1998)

    Article  MATH  Google Scholar 

  8. Sivrikaya-Serifoglu, F., Ulusoy, G.: Parallel machine scheduling with earliness and tardiness penalties. Computers & Operations Research 26, 773–787 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  9. Balakrishan, N., Kanet, J.J., Sridharan, S.V.: Early/tardy scheduling with sequence dependent setups on uniform parallel machines. Computers & Operations Research 26, 127–141 (1999)

    Article  MathSciNet  Google Scholar 

  10. Radhakrishnan, S., Ventura, J.A.: Simulated annealing for parallel machine scheduling with earliness-tardiness penalties and sequence dependent setup times. International Journal of Operational Research 8, 2233–2252 (2000)

    Google Scholar 

  11. Croce, F.D., Trubian, M.: Optimal idle time insertion in early-tardy parallel machines scheduling with precedence constraints. Production Planning & Control 13, 133–142 (2002)

    Article  Google Scholar 

  12. Mendes, A.S., Mller, F.M., Frana, P.M., Moscato, P.: Comparing meta-heuristic approaches for parallel machine scheduling problems. Production Planning & Control 13, 143–154 (2002)

    Article  Google Scholar 

  13. Sun, H., Wang, G.: Parallel machine earliness and tardiness scheduling with proportional weights. Computers & Operations Research 30, 801–808 (2003)

    Article  MATH  Google Scholar 

  14. Zhu, Z., Heady, R.B.: Minimizing the sum of earliness/tardiness in multi-machine scheduling: a mixed integer programming appraoch. Computers & Industrial Engineering 38, 297–305 (2000)

    Article  Google Scholar 

  15. Hopfield, J.: Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences of the USA 81, 3088–3092 (1984)

    Article  Google Scholar 

  16. Hopfield, J., Tank, T.W.: Neural computation of decisions in optimization problems. Biological Cybernetics 52, 141–152 (1985)

    MATH  MathSciNet  Google Scholar 

  17. Watta, P.B.: A coupled gradient network approach for static and temporal mixed-integer optimization. IEEE Transactions on Neural Networks 7, 578–593 (1996)

    Article  Google Scholar 

  18. Smith, K.: Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research. Informs Journal on Computing 11, 15–34 (1999)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Akyol, D.E., Bayhan, G.M. (2005). A Coupled Gradient Network Approach for the Multi Machine Earliness and Tardiness Scheduling Problem. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424925_63

Download citation

  • DOI: https://doi.org/10.1007/11424925_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25863-6

  • Online ISBN: 978-3-540-32309-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics