Skip to main content

Computer simulation in manufacturing systems analysis

  • Chapter
Manufacturing Systems Design and Analysis

Abstract

Computer simulation can be considered as an experimental approach for studying certain functional properties of an organization by experimenting with an appropriate computer model rather than with the real system itself. It is basically an experimental methodology using the power of a computer to process and analyse the large amount of data involved in a problem which otherwise would be extremely difficult to handle (see, for example, Shannon, 1975; Zeigler, 1976; Poole and Szymankiewicz, 1977; Pritsker, 1979; Pritsker and Pegden, 1979; Law and Kelton 1982; Ellison and Wilson, 1984; Gottfried, 1984; Pitt, 1984; Carrie, 1988). It provides an efficient and economical — and sometimes even the only possible — way to analyse a system. Compared with direct real experimentation, the computer simulation approach has the advantages of lower cost, shorter time, greater flexibility and much smaller risk. As a result, this methodology has been extensively used in the area of manufacturing systems studies by both academic researchers and practical engineers.

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.

Further reading

  • Browne, J. and Davies, B.J. (1984), The Design and Validation of a Digital Simulation Model for Job Shop Control Decision Making’, International Journal of Production Research, vol. 22, no. 2, pp. 335–57.

    Article  Google Scholar 

  • Carrie, A. (1988), Simulation of Manufacturing Systems, John Wiley.

    Google Scholar 

  • Ellison, D. and Wilson, J.C.T (1984), How to Write Simulations Using Microcomputers, McGraw-Hill.

    Google Scholar 

  • Ford, D, and Schroer, B. (1987), ‘An Expert Manufacturing Simulation System’, Simulation, May.

    Google Scholar 

  • Ketcham, M. et al., (1989), ‘Information Structures for simulation Modelling of Manufacturing Systems’, Simulation, February.

    Google Scholar 

  • Kiran, A. et al., (1989), ‘An Integrated Simulation Approach to Design of Flexible Manufacturing Systems’, Simulation, February.

    Google Scholar 

  • Law, A.M. and Kelton, W.D. (1982), Simulation Modelling and Analysis, McGraw-Hill.

    Google Scholar 

  • Pitt, M. (1984), Computer Simulation in Management Science, John Wiley.

    Google Scholar 

  • Wildberger, M. (1989), ‘AI and Simulation’, Simulation, July.

    Google Scholar 

Reference

  • Adshead, N.S. and Price, D.H.R. (1986) Experiments on Stock Control Policies and Leadtime Setting Rules, Using an Aggregate Planning Evaluation Model of a Make-For-Stock Shop, Int. J. Prod. Res., 24 (5), 1139–57.

    Article  Google Scholar 

  • Browne, J. and Davies, B.J. (1984) The Design and Validation of a Digital Simulation Model for Job Shop Control Decision Making, Int. J. Prod. Res., 22 (2), 335–57.

    Article  Google Scholar 

  • Carrie, A. (1988) Simulation of Manufacturing Systems, John Wiley.

    Google Scholar 

  • Conway, R.W., Maxwell, W.L. and Miller, L.W. (1967) Theory of Scheduling, Addison-Wesley.

    Google Scholar 

  • Ellison, D. and Wilson, J.C.T. (1984) How to Write Simulations Using Microcomputers, McGraw-Hill.

    Google Scholar 

  • Ford, D. and Schroer, B. (1987) An Expert Manufacturing Simulation System, Simulation, May.

    Google Scholar 

  • Forrester, J.W. (1975) Collected Papers of Jay W. Forrester, The MIT Press.

    Google Scholar 

  • French, S. (1982) Sequencing and Scheduling: An Introduction to the Mathematics of the Job-Shop, John Wiley.

    Google Scholar 

  • Garey, M.R. and Johnson, D.S. (1979) Computers and Intractability: a Guide to the Theory of NP-Completeness, Freeman, San Francisco.

    Google Scholar 

  • Gass, S.I. (1977) Evaluation of Complex Models, Computer & Op. Res., 4, 25.

    Google Scholar 

  • Gottfried, B.S. (1984) Elements of Stochastic Process Simulation, Prentice-Hall, New Jersey.

    Google Scholar 

  • Graham, A.K., et al. (1979) Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey, Ann. Dis. Math., 5, 287–326.

    Article  Google Scholar 

  • Hax, A.C. and Meal, H.C. (1975) Hierarchical Integration of Production Planning and Scheduling, in TIMS Studies in Management Science, Vol. 1, Logistics (ed. M. Geisler), North Holland, New York.

    Google Scholar 

  • Hill, T. and Roberts, S. (1987) A Prototype Knowledge-Based Simulation Support System, Simulation, May.

    Google Scholar 

  • Ketcham, M. et al. (1989) Information Structures for simulation Modelling of Manufacturing Systems, Simulation, Feb.

    Google Scholar 

  • Kiran, A., Schloffer, A. and Hawkins, D. (1989) An Integrated Simulation Approach to Design of Flexible Manufacturing Systems, Simulation, Feb.

    Google Scholar 

  • Law, A.M. and Kelton, W.D. (1982) Simulation Modelling and Analysis, McGraw-Hill.

    Google Scholar 

  • Mellichamp, J. and Wahab, A. (1987) Process Planning Simulation: An FMS Modelling Tool for Engineers, Simulation, May.

    Google Scholar 

  • Murray, K. and Sheppard, S. (1988) Knowledge-Based Simulation Model Specification, Simulation, March.

    Google Scholar 

  • Pidd, M. (1984) Computer Simulation in Management Science, John Wiley.

    Google Scholar 

  • Poole, T. and Szymankiewicz, J. (1977) Using Simulation to Solve Problems, McGraw-Hill.

    Google Scholar 

  • Pritsker, A.A.B. (1979) Modelling and Analysis Using Q-GERT Networks, John Wiley.

    Google Scholar 

  • Pritsker, A.A.B. and Pegden, C.D. (1979) An Introduction to Simulation and SLAM, Halstead Press.

    Google Scholar 

  • Shannon, R.E. (1975) Systems Simulation — the Art and Science, Prentice-Hall.

    Google Scholar 

  • Smith, R. and Platt, L. (1988) Benefits of Animation in the Simulation of an Assembly Line Simulation.

    Google Scholar 

  • Ullman, J.D. (1976) Complexity of Sequencing Problems, in Computer and Job-Shop Scheduling Theory (ed. E.G. Coffman jr) pp. 134–164, John Wiley, New York.

    Google Scholar 

  • Van Horn, R.L. (1971) Validation of Simulation Results Management Science, 17 (5), 247–58.

    Article  Google Scholar 

  • White, D. (1988) PCModel User’s Guide, Simulation Software Systems.

    Google Scholar 

  • Wu, B. (1990) Large-scale Computer Simulation and Systems Design of Computer-Integrated Manufacturing, Computer-Integrated Manufacturing Systems, 3 (2), 100–10.

    Article  Google Scholar 

  • Zeigler, B.P. (1976) Theory of Modelling and Simulation, Wiley-Interscience, New York.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 1992 B. Wu

About this chapter

Cite this chapter

Wu, B. (1992). Computer simulation in manufacturing systems analysis. In: Manufacturing Systems Design and Analysis. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3128-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-94-011-3128-5_5

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-0-412-40840-3

  • Online ISBN: 978-94-011-3128-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics