Skip to main content
SpringerLink
Log in

Ordinal Hill Climbing Algorithms for Discrete Manufacturing Process Design Optimization Problems

  • Published:
Discrete Event Dynamic Systems Aims and scope Submit manuscript

Abstract

Thispaper introduces ordinal hill climbing algorithms for addressingdiscrete manufacturing process design optimization problems usingcomputer simulation models. Ordinal hill climbing algorithmscombine the search space reduction feature of ordinal optimizationwith the global search feature of generalized hill climbing algorithms.By iteratively applying the ordinal optimization strategy withinthe generalized hill climbing algorithm framework, the resultinghybrid algorithm can be applied to intractable discrete optimizationproblems. Computational results on an integrated blade rotormanufacturing process design problem are presented to illustratethe application of the ordinal hill climbing algorithm. The relationshipbetween ordinal hill climbing algorithms and genetic algorithmsis also discussed. This discussion provides a framework for howthe ordinal hill climbing algorithm fits into currently appliedalgorithms, as well as to introduce a bridge between the twoalgorithms.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Chen, C. H., and Kumar, V. 1996. Motion planning of walking robots in environments with uncertainty. Proceedings of IEEE Conference on Robotics and Automation.

  • Dai, L. 1996. Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems. Journal of Optimization Theory and Application 91(2): 363-388.

    Google Scholar 

  • Fischer, C. E., Gunasakera, J. S., and Malas, J. C. 1997. Process model development for optimization of forged disk manufacturing processes. Steel Forgings, Second Volume, ASTM STP 1257. E. G. Nisbett and A. S. Melilli, eds. American Society for Testing and Materials.

  • Gunasakera, J. S., Fischer, C. E., Malas, J. C., Mullins, W. M., and Yang, M. S. 1996. Development of process models for use with global optimization of a manufacturing system. Proceedings of the ASME Symposium on Modeling, Simulation, and Control of Metal Processing, ASME-International Mechanical Engineering Congress, Atlanta, GA, November.

  • Ho, Y. C., Sreenivas, R., and Vakili, P. 1992. Ordinal optimization of discrete event dynamic systems. Journal of Discrete Event Dynamical Systems 2(2): 61-88.

    Google Scholar 

  • Jacobson, S. H., Sullivan, K. A., and Johnson, A. W. 1997. Generalized hill climbing algorithms for discrete manufacturing process design problems using computer simulation models. Proceedings of the 11th European Simulation Multiconference, Istanbul, Turkey, June 1-4, pp. 473-478.

  • Jacobson, S. H., Sullivan, K. A., and Johnson, A. W. 1998. Discrete manufacturing process design optimization using computer simulation and generalized hill climbing algorithms. Engineering Optimization 31: 247-260.

    Google Scholar 

  • Johnson, A. W. 1996. Generalized hill climbing algorithms. Ph.D. Dissertation, Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia.

    Google Scholar 

  • Johnson, A. W., and Jacobson, S. H. 2001a. A class of convergent generalized hill climbing algorithms. Applied Mathematics and Computation (to appear).

  • Johnson, A. W., and Jacobson, S. H. 2001b. A general convergence result for hill climbing algorithms. Discrete Applied Mathematics (to appear).

  • Lee, L. H., Lau, T. W. E., and Ho, Y. C. 1999. Explanation of goal softening in ordinal optimization. IEEE Transactions on Automatic Control 44(1): 94-99.

    Google Scholar 

  • Medina, E. A. 1998. Cost function for discrete event dynamical system optimization program. Technical Report, Austral Engineering and Software, Inc., Athens, Ohio.

    Google Scholar 

  • Reeves, C. R. 1993. Modern Heuristic Techniques for Combinatorial Problems. New York: John Wiley & Sons, Inc.

    Google Scholar 

  • Yang, M. S., Lee, L. H., and Ho, Y. C. 1997. On stochastic optimization and its applications to manufacturing. Proceedings of AMS Conference on Stochastic Problems in Manufacturing, Lecture Notes in Applied Mathematics 33, pp. 317-331.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. United Airlines, Chicago, Illinois

    Kelly A. Sullivan

  2. Department of Mechanical and Industrial Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, 61801

    Sheldon H. Jacobson

Authors
  1. Kelly A. Sullivan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sheldon H. Jacobson
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sullivan, K.A., Jacobson, S.H. Ordinal Hill Climbing Algorithms for Discrete Manufacturing Process Design Optimization Problems. Discrete Event Dynamic Systems 10, 307–324 (2000). https://doi.org/10.1023/A:1008302003857

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008302003857

Search

Navigation