Journal of the Operational Research Society

, Volume 56, Issue 6, pp 619–629 | Cite as

Discrete-event simulation: from the pioneers to the present, what next?

Review

Abstract

Discrete-event simulation is one of the most popular modelling techniques. It has developed significantly since the inception of computer simulation in the 1950s, most of this in line with developments in computing. The progress of simulation from its early days is charted with a particular focus on recent history. Specific developments in the past 15 years include visual interactive modelling, simulation optimization, virtual reality, integration with other software, simulation in the service sector, distributed simulation and the use of the worldwide web. The future is then speculated upon. Potential changes in model development, model use, the domain of application for simulation and integration with other simulation approaches are all discussed. The desirability of continuing to follow developments in computing, without significant developments in the wider methodology of simulation, is questioned.

Keywords

discrete-event simulation computing 

References

  1. Jeffrey P and Seaton R (1995). The use of operational research tools: a survey of operational research practitioners in the UK. J Opl Res Soc 46(7): 797–808.CrossRefGoogle Scholar
  2. Fildes R and Ranyard JC (1997). Success and survival of operational research groups — a review. J O Res Soc 48(4): 336–360.CrossRefGoogle Scholar
  3. Clark DN (1999). Strategic level MS/OR tool usage in the United Kingdom and New Zealand: a comparative survey. Asia-Pacific J Opl Res 16(1): 35–51.Google Scholar
  4. Wild R (2002). Operations Management, 6th edn. Continuum: London.Google Scholar
  5. Pidd M (1984). Computer simulation for operational research in 1984. In: Eglese R and Rand G (eds). Developments in Operational Research. Pergammon Press: Oxford, UK, pp 19–30.CrossRefGoogle Scholar
  6. Bell PC and O'Keefe RM (1987). Visual interactive simulation — history, recent developments, and major issues. Simulation 49(3): 109–116.CrossRefGoogle Scholar
  7. Paul RJ (1991). Recent developments in simulation modelling. J Opl Res Soc 42(3): 217–226.CrossRefGoogle Scholar
  8. Bell PC (1991). Visual interactive modelling: the past, the present, and the prospects. Eur J Opl Res 54: 274–286.CrossRefGoogle Scholar
  9. Hollocks BW (2004). Still simulating after all these years — reflections on 40 years in Simulation. In: Brailsford SC, Oakshott L, Robinson S and Taylor SJE (eds). Proceedings of the 2004 Operational Research Society Simulation Workshop (SW04). Operational Research Society, Birmingham, pp 209–222.Google Scholar
  10. Araten M et al (1992). The winter simulation conference: perspectives of the founding fathers. In: Swain JJ, Goldsman D, Crain RC and Wilson JR (eds). Proceedings of the 1992 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 37–63.Google Scholar
  11. Banks J, Carson JS, Nelson BL and Nicol DM (2001). Discrete-Event System Simulation, 3rd edn. Prentice-Hall: Upper Saddle River, NJ.Google Scholar
  12. Nance RE and Sargent RG (2002). Perspectives on the evolution of simulation. Operations Research 50(1): 161–172.CrossRefGoogle Scholar
  13. Tocher KD (1963). The Art of Simulation. The English Universities Press: London.Google Scholar
  14. Schriber T (1974). Simulation Using GPSS. Wiley: New York.Google Scholar
  15. Markowitz HM, Hausner B and Karr HW (1962). SIMSCRIPT: The Simulation Programming Language. RAND Corporation, Cambridge, MA, RM-3310.Google Scholar
  16. Dahl O and Nygaard K (1966). SIMULA: an algol-based simulation language. Comm ACM 9(9): 671–678.CrossRefGoogle Scholar
  17. Amiry AP (1965). The simulation of information flow in a steelmaking plant. In: Hollingdale S (ed). Digital Simulation in Operational Research. English University Press, London, pp 347–356.Google Scholar
  18. Pritsker AAB and Pegden CD (1979). Introduction to Simulation and SLAM. Wiley: New York.Google Scholar
  19. Wolverine . http://www.wolverinesoftware.com/wolverin.htm, accessed March 2004.
  20. Hurrion RD (1976). The design, use and required facilities of an interactive visual computer simulation language to explore production planning problems. PhD thesis, University of London.Google Scholar
  21. Fiddy E, Bright JG and Hurrion RD (1981). SEE-WHY: interactive simulation on the screen. Proceedings of the Institute of Mechanical Engineers C293/81, I. Mech. E., London, pp 167–172.Google Scholar
  22. Gilman AR and Billingham C (1989). A tutorial on SEE WHY and WITNESS. In: MacNair EA, Musselman KJ and Heidelberger P (eds). Proceedings of the 1989 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 192–200.Google Scholar
  23. Szymankiewicz J, McDonald J and Turner K (1988). Solving Business Problems by Simulation. McGraw-Hill: Maidenhead, UK.Google Scholar
  24. Concannon K and Becker P (1990). A tutorial on GENETIK simulation and scheduling. In: Balci O, Sadowski RP and Nance RE (eds). Proceedings of the 1990 Winter Simulation Conference, IEEE, Picataway, NJ, pp 140–145.Google Scholar
  25. Sturrock DT and Pegden CD (1989). Introduction to SIMAN. In: MacNair EA, Musselman KJ and Heidelberger P (eds). Proceedings of the 1989 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 129–139.Google Scholar
  26. Poorte JP and Davis DA (1989). Computer animation with CINEMA. In: MacNair EA, Musselman KJ and Heidelberger P (eds). Proceedings of the 1989 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 147–154.Google Scholar
  27. Harrell CR and Tumay K (1990). ProModel PC tutorial. In: Balci O, Sadowski RP and Nance RE (eds). Proceedings of the 1990 Winter Simulation Conference. IEEE, Picataway, NJ, pp 128–131.Google Scholar
  28. Hollocks B (1992). A well-kept secret? Simulation in Manufacturing Industry Reviewed. OR Insight 5(4): 12–17.Google Scholar
  29. Pegden CD and Davis DA (1992). Arena: a SIMAN/cinema-based hierarchical modeling system. In: Swain JJ, Goldsman D, Crain RC and Wilson JR (eds). Proceedings of the 1992 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 390–399.Google Scholar
  30. Hugan JC (1995). QUEST – queueing event simulation tool. In: Alexopoulos C, Kang K, Lilegdon WR and Goldsman D (eds). Proceedings of the 1995 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 432–436.Google Scholar
  31. Nordgren WB (1995). Taylor II manufacturing Simulation Software. In: Alexopoulos C, Kang K, Lilegdon WR and Goldsman D (eds). Proceedings of the 1995 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 401–404.Google Scholar
  32. Rohrer MW (1996). AutoMod Tutorial. In: Charnes JM, Morrice DM, Brunner DT and Swain JJ (eds). Proceedings of the 1995 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 500–505.Google Scholar
  33. Pritsker AAB and O'Reilly JJ (1996). AweSim: the integrated simulation system. In: Charnes JM, Morrice DM, Brunner DT and Swain JJ (eds). Proceedings of the 1996 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 481–484.Google Scholar
  34. Barnes CD and Laughery Jr KR (1997). Advanced uses for micro saint simulation software. In: Andradóttir S, Healy KJ, Withers DH and Nelson BL (eds). Proceedings of the 1997 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 680–686.Google Scholar
  35. Hullinger DR (1999). Taylor enterprise dynamics. In: Farrington PA, Nembhard HB, Sturrock DT and Evans GW (eds). Proceedings of the 1999 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 227–229.Google Scholar
  36. Nordgren WB (2002). Flexsim simulation environment. In: Yücesan E, Chen C-H, Snowden SL and Charnes JM (eds). Proceedings of the 2002 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 250–252.Google Scholar
  37. Pidd M (1998). Computer Simulation in Management Science, 4th edn. Wiley: Chichester, UK.Google Scholar
  38. Simul8. http://www.simul8.com, accessed March 2004.
  39. Krahl D (1996). Modeling with extend. In: Charnes JM, Morrice DM, Brunner DT and Swain JJ (eds). Proceedings of the 1996 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 578–583.Google Scholar
  40. ShowFlow. http://www.showflow.co.uk, accessed March 2004.
  41. Law AM and McComas MG (2002). Simulation-based optimization. In: Yücesan E, Chen C-H, Snowden SL and Charnes JM (eds). Proceedings of the 2002 Winter Simulation Conference. EEE, Piscataway, NJ, pp 41–44.Google Scholar
  42. Law AM and Kelton WD (2000). Simulation Modeling and Analysis, 3rd edn. McGraw-Hill: New York.Google Scholar
  43. Fu MC (2002). Optimization for simulation: theory vs practice. INFORMS J Comput 14(3): 192–215.CrossRefGoogle Scholar
  44. Olafsson S and Kim J (2002). Simulation optimization. In: Yücesan E, Chen C-H, Snowden SL and Charnes JM (eds). Proceedings of the 2002 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 79–84.Google Scholar
  45. Hurrion RD (2000). A sequential method for the development of visual interactive meta-simulation models using neural networks. J Opl Res Soc 51(6): 712–719.CrossRefGoogle Scholar
  46. Robinson S (2004). Simulation: The Practice of Model Development and Use. Wiley: Chichester, UK.Google Scholar
  47. Lanner. http://www.lanner.co.uk,accessed March 2004.
  48. Harrell CR and Price RN (2003). Simulation modeling using promodel technology. In: Chick S, Sanchez PJ, Ferrin D and Morrice DJ (eds). Proceedings of the 2003 Winter Simulation Conference. IEEE, Picataway, NJ, pp 175–181.Google Scholar
  49. Rohrer MW and McGregor IW (2002). Simulating reality using AutoMod. In: Yücesan E, Chen C-H, Snowden SL and Charnes JM (eds). Proceedings of the 2002 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 173–181.Google Scholar
  50. April J, Glover F, Kelly J and Laguna M (2001). Simulation/Optimization Using ‘Real-World’ Applications. In: Peters BA, Smith JS, Medeiros DJ and Rohrer MW (eds). Proceedings of the 2001 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 134–138.Google Scholar
  51. Waller AP and Ladbrook J (2002). Experiencing virtual factories of the future. In: Yücesan E, Chen C-H, Snowden SL and Charnes JM (eds). Proceedings of the 2002 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 513–517.Google Scholar
  52. Preddy SM and Nance RE (2002). Key requirements for CAVE simulations. In: Yücesan E, Chen C-H, Snowden SL and Charnes JM (eds). Proceedings of the 2002 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 127–135.Google Scholar
  53. Flitman AM and Hurrion RD (1987). Linking discrete-event simulation models with expert systems. J Opl Res Soc 38(8): 723–734.CrossRefGoogle Scholar
  54. O'Keefe RM (1989). The Role of artificial intelligence in discrete-event simulation. In: Widman LE, Loparo KA and Neilsen NR (eds). Artificial Intelligence, Simulation and Modeling. Wiley, New York, pp 359–379.Google Scholar
  55. Moffat J (2000). Representing the command and control process in simulation models of conflict. J Opl Res Soc 51(4): 431–439.CrossRefGoogle Scholar
  56. Baines TS and Kay JM (2002). Human performance modelling as an aid in the process of manufacturing system design: a pilot study. Int J Prod Res 40(10): 2321–2334.CrossRefGoogle Scholar
  57. Robinson S et al (2003). Modelling and improving maintenance decisions: having foresight with simulation and artificial intelligence. SAE 2002 Trans J Mater Manuf, VIII(5), 256–264.Google Scholar
  58. Gatersleben M and van der Weij SW (1999). Analysis and simulation of passenger flows in an airport terminal. In: Farrington PA, Nembhard HB, Sturrock DT and Evans GW (eds). Proceedings of the 1999 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 1226–1231.Google Scholar
  59. Anton J (1999). Call Center Performance Enhancement Using Simulation and Modeling. Purdue University Press: West Lafayette, IN.Google Scholar
  60. Melao N and Pidd M (2003). Use of business process simulation: a survey of practitioners. J Opl Res Soc 54(1): 2–10.CrossRefGoogle Scholar
  61. Hueter J and Swart W (1998). An integrated labor-management system for Taco bell. Interfaces 28(1): 75–91.CrossRefGoogle Scholar
  62. Jun JB, Jacobson SH and Swisher JR (1999). Application of discrete-event simulation in health care clinics: a survey. J Opl Res Soc 50(2): 109–123.CrossRefGoogle Scholar
  63. Hlupic V and Robinson S (1998). Business process modelling and analysis using discrete-event simulation. In: Medeiros DJ, Watson EF, Manivannan M and Carson J (eds). Proceedings of the 1998 Winter Simulation Conference 1998 Piscataway, NJ, pp 1363–1369.Google Scholar
  64. Fishwick PA (1996). Web-based simulation: some personal observations. In: Charnes JM, Morrice DM, Brunner DT and Swain JJ (eds). Proceedings of the 1996 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 772–779.Google Scholar
  65. Buss AH and Stork KA (1996). Discrete event simulation on the world wide web using Java. In: Charnes JM, Morrice DM, Brunner DT and Swain JJ (eds). Proceedings of the 1996 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 780–785.Google Scholar
  66. Nair RS, Miller JA and Zhang Z (1996). Java-based query driven simulation environment. In: Charnes JM, Morrice DM, Brunner DT and Swain JJ (eds). Proceedings of the 1996 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 786–793.Google Scholar
  67. Koh K-H, de Souza R and Ho N-C (1996). Multi-processor distributed simulation for job-shop scheduling: boon or bane? Int J Comput Integr Manuf 9(6): 434–442.CrossRefGoogle Scholar
  68. Rao DM and Wilsey PA (2002). An ultra-large-scale simulation framework. Journal of Parallel Distrib Comput 62: 1670–1693.CrossRefGoogle Scholar
  69. Gabbar HA, Shinohara S, Shimada Y and Suzuki K (2003). Experiment on distributed dynamic simulation for safety design of chemical plants. Simul Modell Pract Theory 11: 109–123.CrossRefGoogle Scholar
  70. Fujii S, Kaihara T and Morita H (2000). A distributed virtual factory in agile manufacturing Environment. Int J Prod Res 38(17): 4113–4128.CrossRefGoogle Scholar
  71. Zülch G, Jonsson U and Fischer J (2002). Hierarchical simulation of complex production systems by coupling models. Int J Prod Econ 77: 39–51.CrossRefGoogle Scholar
  72. Korn S, Burns GR and Harrison DK (1999). The application of multiparadigm simulation techniques to manufacturing processes. Int J Adv Manuf Technol 15: 869–875.CrossRefGoogle Scholar
  73. Davis WJ (1998). On-line simulation: need and evolving research requirements. In: Banks J (ed). Handbook of Simulation. Wiley, New York, pp 465–516.CrossRefGoogle Scholar
  74. Anagnostopoulos D and Nikolaidou M (2003). Executing a minimum number of replications to support the reliability of FRTS predictions. In: Turner SJ and Taylor SJE (eds). Proceedings of the seventh IEEE International Symposium on Distributed Simulation and Real-Time Applications. IEEE Computer Society, Los Alamitos, CA, pp 138–146.Google Scholar
  75. Biles WE and Kleijnen JPC (2003). Statistical methodology for WEB-based simulation. In: Turner SJ and Taylor SJE (eds). Proceedings of the seventh IEEE International Symposium on Distributed Simulation and Real-Time Applications IEEE Computer Society, Los Alamitos, CA, pp 147–149.Google Scholar
  76. Yücesan E, Luo Y-C, Chen C-H and Lee I (2001). Distributed web-based simulation experiments for optimization. Simul Pract Theory 9: 73–90.CrossRefGoogle Scholar
  77. Paris J-L and Pierreval H (2001). A distributed evolutionary simulation optimization approach for configuration of multiproduct Kanban systems. Int J Comp Integr Manuf 14(5): 421–430.CrossRefGoogle Scholar
  78. Mizuta H and Yamagata Y (2002). Transaction cycle of agents and web-based gaming simulation for international emissions trading. In: Yücesan E, Chen C-H, Snowden SL and Charnes JM (eds). Proceedings of the 2002 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 801–806.Google Scholar
  79. Jacobs PHM, Lang NA and Verbraeck A (2002). D-SOL; A distributed Java based discrete event simulation architecture. In: Yücesan E, Chen C-H, Snowden SL and Charnes JM (eds). Proceedings of the 2002 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 793–800.Google Scholar
  80. Pooley R and Wilcox P (2000). Distributing decision making using Java simulation across the world wide web. J Opl Res Soc 51(4): 395–404.CrossRefGoogle Scholar
  81. Wang Y-H and Liao Y-C (2003). Implementation of a collaborative web-based simulation modeling environment. In: Turner SJ and Taylor SJE (eds). Proceedings of the seventh IEEE International Symposium on Distributed Simulation and Real-Time Applications IEEE Computer Society, Los Alamitos, CA, pp 150–157.Google Scholar
  82. Kilgore RA (2001). Open-source SML and silk for Java-based, object-oriented simulation. In: Peters BA, Smith JS, Medeiros DJ and Rohrer MW (eds). Proceedings of the 2001 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 262–268.Google Scholar
  83. Taylor SJE, Robinson S and Ladbrook J (2003). Towards collaborative simulation modelling: improving human-to-human interaction through groupware. In: Al-Dabass D (ed). Proceedings of the 17th European Simulation Multiconference (ESM 2003). Society for Computer Simulation, Delft, pp 474–482.Google Scholar
  84. Linebarger JM, Janneck CD and Kessler GD (2003). Shared simple virtual environment: an object-oriented framework for highly interactive group working. In: Turner SJ and Taylor SJE (eds). Proceedings of the seventh IEEE International Symposium on Distributed Simulation and Real-Time Applications IEEE Computer Society, Los Alamitos, CA, pp 170–180.Google Scholar
  85. Paul RJ and Taylor SJE (2002). What use is model reuse: is there a crook at the end of the rainbow?. In: Yücesan E, Chen C-H, Snowden SL and Charnes JM (eds). Proceedings of the 2002 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 648–652.Google Scholar
  86. Pidd M (2002). Simulation software and model reuse: a polemic. In: Yücesan E, Chen C-H, Snowden SL and Charnes JM (eds). Proceedings of the 2002 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 772–775.Google Scholar
  87. International Review of Operational Research in the United Kingdom. Kensington Hilton, London, 5 March 2004. unpublished.Google Scholar
  88. Edwards JS et al (2004). Using a simulation model for knowledge elicitation and knowledge management. Simul Modell Pract Theory, Forthcoming.Google Scholar
  89. Brooks RJ and Tobias AM (1996). Choosing the best model: level of detail, complexity and model performance. Math Comput Modell 24(4): 1–14.CrossRefGoogle Scholar
  90. Eldabi T, Robinson S, Taylor SJE and Wilcox PA (eds) (2002). Proceedings of the Operational Research Society Simulation Workshop. Operational Research Society: Birmingham, UK.Google Scholar
  91. Brailsford SC, Oakshott L, Robinson S and Taylor SJE (eds) (2004). Proceedings of the 2004 Operational Research Society Simulation Workshop (SW04). Operational Research Society: Birmingham, UK.Google Scholar
  92. Chick S, Sanchez PJ, Ferrin D and Morrice DJ (eds) (2003). Proceedings of the 2003 Winter Simulation Conference. IEEE: Picataway, NJ.Google Scholar
  93. Solomon (700 BC). Proverbs Chapter 29, Verse 18. In: The Bible, Authorized King James Version. Cambridge University Press, Cambridge, UK.Google Scholar
  94. Amory A, Naicker K, Vincent J and Adams C (1999). The use of computer games as an educational tool: identification of appropriate game types and game elements. Br J Educ Technol 30(4): 311–321.CrossRefGoogle Scholar
  95. Barton RR et al (2003). Panel: simulation — past, present and future. In: Chick S, Sanchez PJ, Ferrin D and Morrice DJ (eds). Proceedings of the 2003 Winter Simulation Conference. IEEE, Picataway, NJ, pp 2044–2050.Google Scholar
  96. Pidd M (1992). Object orientation and three phase simulation. In: Swain JJ, Goldsman D, Crain RC and Wilson JR (eds). Proceedings of the 1992 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 689–693.Google Scholar
  97. Law AM (1991). Simulation model's level of detail determines effectiveness. Industrial Engineering 23(10): 16–18.Google Scholar
  98. Sargent RG (1992). Validation and verification of simulation models. In: Swain JJ, Goldsman D, Crain RC and Wilson JR (eds). Proceedings of the 1992 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 104–114.Google Scholar
  99. Balci O (1994). Validation, verification, and testing techniques throughout the life cycle of a simulation study. Ann Opns Res 53: 121–173.CrossRefGoogle Scholar
  100. Balci O (1997). Principles of simulation model validation, verification, and testing. Trans Soc Comput Simul 14(1): 3–12.Google Scholar
  101. Robinson S (1999). Simulation verification, validation and confidence: a tutorial. Trans Soc Comp Simul 16(2): 63–69.Google Scholar
  102. Robinson S et al (2004). Simulation model reuse: definitions, benefits and obstacles. Simul Modell Pract Theory, Forthcoming.Google Scholar
  103. Clementson AT (1986). Simulating with activities using CAPS/ECSL (the British approach to discrete-event simulation). In: Wilson J, Henriksen J and Roberts S (eds). Proceedings of the 1986 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 113–122.Google Scholar
  104. Balmer D and Paul RJ (1986). CASM — the right environment for simulation. Journal of the Operational Research Society 37(5): 443–452.Google Scholar
  105. Arief LB and Speirs NA (2000). A UML tool for an automatic generation of simulation programs. In: Proceedings of the second International Workshop on Software and Performance (WOSP 2000) ACM Press: New York, pp 71–76.Google Scholar
  106. Hollocks BW (2001). Discrete-event simulation: an inquiry into user practice. Simulation Practice and Theory 8: 451–471.CrossRefGoogle Scholar
  107. Hlupic V (1999). Discrete-event simulation software: what the users want. Simulation 73(6): 362–370.CrossRefGoogle Scholar
  108. Banks J et al (2003). The future of the simulation industry. In: Chick S, Sanchez PJ, Ferrin D and Morrice DJ (eds). Proceedings of the 2003 Winter Simulation Conference. IEEE, Picataway, NJ, pp 2033–2043.Google Scholar
  109. Williams T (1996). Simulating the man-in-the-loop. OR Insight 9(4): 17–21.CrossRefGoogle Scholar
  110. Lyu J and Gunasekaran A (1997). An intelligent simulation model to evaluate scheduling strategies in a steel company. Int J Syst Sci 28(6): 611–616.CrossRefGoogle Scholar
  111. Brailsford S and Schmidt B (2003). Towards Incorporating human behaviour in models of health care systems: an approach using discrete event simulation. Eur J Opl Res 150: 19–31.CrossRefGoogle Scholar
  112. Robinson S (2001). Soft with a hard centre: discrete-event simulation in facilitation. J Opl Res Soc 52(8): 905–915.CrossRefGoogle Scholar
  113. Lane DC (1999). Social theory and system dynamics practice. Eur J Opl Res 113: 501–527.CrossRefGoogle Scholar
  114. Morecroft J (2004). Mental models and learning in system dynamics practice. In: Pidd M (ed). Systems Modelling: Theory and Practice. Wiley, Chichester, UK, pp 101–126.Google Scholar
  115. Pidd M (2003). Tools for Thinking: Modelling in Management Science, 2nd edn. Wiley: Chichester, UK.Google Scholar
  116. Innis G and Rexstad E (1983). Simulation model simplification techniques. Simulation 41(1): 7–15.CrossRefGoogle Scholar
  117. Ward SC (1989). Arguments for constructively simple models. J Opl Res Soc 40(2): 141–153.CrossRefGoogle Scholar
  118. Salt J (1993). Simulation should be easy and fun. In: Evans GW, Mollaghasemi M, Russell EC and Biles WE (eds). Proceedings of the 1993 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 1–5.Google Scholar
  119. Chwif L, Barretto MRP and Paul RJ (2000). On simulation model complexity. In: Joines JA, Barton RR, Kang K and Fishwick PA (eds). Proceedings of the 2000 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 449–455.Google Scholar

Copyright information

© Palgrave Macmillan Ltd 2004

Authors and Affiliations

  1. 1.University of WarwickCoventryUK

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