Skip to main content

Simulation Metamodeling of a Maintenance Float System

  • Chapter
Maintenance, Modeling and Optimization
  • 819 Accesses

Abstract

In this chapter, we discuss the use of metamodels in analyzing maintenance float systems. Metamodels are, increasingly, being used in solving complex problems primarily because of there ease of use and tremendous appeal for practical purposes. Further, metamodels utilize the increasing power of PC-based simulations and statistical applications. Our focus here is on their application to maintenance float network problems. Maintenance float problems can be considered as part of closed queuing network problems. Such problems are very difficult to model analytically. With the use of simulation, we can better understand maintenance float problems and with metamodels, we may be able to provide some generalizations to the results obtained through simulation.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Law, M., and McComas, M. G. Pitfalls to Avoid in the Simulation of Manufacturing Systems. Ind. Engng 31(5) (1989) 28–31.

    Google Scholar 

  2. Friedman, L.W. and Pressman, I. The Metamodel in Simulation Analysis: Can it be Trusted? J. Opt.Res. Soc 39(10) (1968) 939–948.

    Google Scholar 

  3. Kleijnen, J.P.C. Statistical Tools for Simulation Practitioners. Marcel Dekket, New York 1987.

    MATH  Google Scholar 

  4. Kleijnen, J.P.C., and Stanridge, C. R. Experimental Design and Regression Analysis in Simulation: an FMS Case Study. European Journal of Operations Research. 33(1988) 257–261.

    Article  Google Scholar 

  5. Georgantzas, N.C., and Madu, C.N. Maintenance Float Policy Estimation with Imperfect Information. Computers Ind. Engrg l6(2)(1988) 257–268.

    Google Scholar 

  6. Levine, B. Estimating Maintenance Float Factors on the Basis of Reliability Theory. Ind. Qual. Control, 21(2) (1965) 401–405.

    Google Scholar 

  7. Lowe, P.H., and Lewis, W. Reliability Analysis Based on the Weibull Distribution: an Application to Maintenance Floar Factors. International Journal of Production Research, 21(4) (1983) 461–470.

    Article  Google Scholar 

  8. C.N. Madu. Determination of Maintenance Floats Using Buzens Algorithm. International Journal of Production Research, 26(1988) 1385–1394.

    Article  MATH  Google Scholar 

  9. Madu, C.N., and Georgantzas, N.C. Waiting Line Effects in Analytic Maintenance Float Policy. Decision Sci, 19(3) (1988) 521–534.

    Article  Google Scholar 

  10. Madu, C.N. The Study of a Maintenance Float Model with Gamma Failure Distribution. International Journal of Production Research, 25(9), (1987) 1305–1323.

    Article  MATH  Google Scholar 

  11. Koeningsberg, E. Finite Queues and Cyclic Queues. Operations Research, 8(1960) 246–253.

    Article  MathSciNet  Google Scholar 

  12. Koeningsberg, E. Cyclic Queues. Operations Research, 9(1958) 22–35.

    Article  Google Scholar 

  13. Madu, C.N., Chanin, M.N., Georgantzas, N.C., and Kuei, C.H. Coefficient of Variation: a Critical Factor in Maintenance Float Policy. Computers Operations Research, 17(2) (1990) 177–185.

    Article  Google Scholar 

  14. Madu, C.N. A Closed Queueing Maintenance Network with Two Repair Centers. Journal of Operations Research Society, 39(10) (1988) 959–967.

    MATH  Google Scholar 

  15. Gross, D., Miller, D.R., and Soland, T.M. A Closed Queuing Network Model for Multiechelon Repairable Item Provisioning. IIE Transactions, 15(4) (1983) 344–352.

    Article  Google Scholar 

  16. Madu, C.N., and Chanin, M.N. Maintenance Float Analysis: a Regression Metamodel A roach. Working Paper, Pace University, New York, 1989.

    Google Scholar 

  17. Gordon, W.J., and Newell, G.F. Closed Queueing Systems with Exponential Servers. Operations Research, 15(2) (1967) 254–265.

    Article  MATH  Google Scholar 

  18. Buzen, J.P. Computational Algorithms for Closed Queuing Networks with Exponintial Servers. Commun. A CM 16(9) (1973) 527–531.

    MathSciNet  MATH  Google Scholar 

  19. Box, G.E.P., Hunter, W.G., and Hunter, J.S. Statistics for Experimenters. Wiley, New York, 1978.

    MATH  Google Scholar 

  20. Dey, A. Orthogonal Fractional Factorial Designs. John Wiley and sons, New York, 1985.

    MATH  Google Scholar 

  21. Friedman, L.W., and Friedman, H.H. Statistical Considerations in Computer Simulation: the State of the Art. J. Statistic. Comput. Simul 19(1984) 237–263.

    Article  MATH  Google Scholar 

  22. Gardenier, T. K. PRE_PRIM as a Pre-processor to Simulations: a Cohesive Unit. Simulation (1990) 65–70.

    Google Scholar 

  23. Hunter, J.S., and Naylor, T.H. Experimental Designs for Computer Simulation Experiments. Management Sci 16(7) (1970) 422–434.

    Article  Google Scholar 

  24. Kuei, C.H., and Madu, C.N. Polynomial Decomposition and Taguchi Design for Maintenance Float System. European Journal of Operations Research, 72(1994) 364–375.

    Article  MATH  Google Scholar 

  25. Madu, C. N. Simulation in Manufacturing: a Regression Metamodel approach. Computers and Industrial Engineering, 18(3)(1990) 381–389.

    Article  Google Scholar 

  26. Madu, C.N., and Kuei, C.H. Group Screening and Taguchi Design Applications in a Multiechelon Maintenance Float Policy. Computers Operations Research, 19(2)(1992) 95–105.

    Article  MATH  Google Scholar 

  27. Schmidt, M.S., and Meile, L.C. Taguchi Designs and Linear Programming Speed New Product Formulation. Interfaces, 19(5) (1989) 49–56.

    Article  Google Scholar 

  28. Madu, I.E., and Madu, C.N. Design Optimization using Signal-to-Noise Ratio. Simulation, 1999.

    Google Scholar 

  29. Madu, C.N., and Kuei, C.H. Experimental Statistical Designs and Analysis in Simulation Modeling. Quorum Books, Westport, Connecticut, 1993.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer Science+Business Media New York

About this chapter

Cite this chapter

Madu, C.N. (2000). Simulation Metamodeling of a Maintenance Float System. In: Ben-Daya, M., Duffuaa, S.O., Raouf, A. (eds) Maintenance, Modeling and Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4329-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-4329-9_6

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-6944-8

  • Online ISBN: 978-1-4615-4329-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics