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Conceptual modelling for simulation Part II: a framework for conceptual modelling

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Journal of the Operational Research Society

Abstract

Following on from the definition of a conceptual model and its requirements laid out in a previous paper, a framework for conceptual modelling is described. The framework consists of five iterative activities: understanding the problem situation, determining the modelling and general project objectives, identifying the model outputs, identify the model inputs, and determining the model content. The framework is demonstrated with a modelling application at a Ford Motor Company engine assembly plant. The paper concludes with a discussion on identifying data requirements from the conceptual model and the assessment of the conceptual model.

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References

  • Balci O and Nance RE (1985). Formulated problem verification as an explicit requirement of model credibility. Simulation 45: 76–86.

    Article  Google Scholar 

  • Baldwin LP, Eldabi T and Paul RJ (2004). Simulation in healthcare management: A soft approach (MAPIU). Simul Model Pract Theory 12: 541–557.

    Article  Google Scholar 

  • Checkland PB (1981). Systems Thinking, Systems Practice. Wiley: Chichester, UK.

    Google Scholar 

  • Courtois PJ (1985). On time and space decomposition of complex structures. Commun ACM 28: 590–603.

    Article  Google Scholar 

  • Crapo AW, Waisel LB, Wallace WA and Willemain TR (2000). Visualization and the process of modeling: A cognitive-theoretic view. In: Ramakrishnan R, Stolfo S, Bayardo R and Parsa I (eds). Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press: New York, pp. 218–226.

    Chapter  Google Scholar 

  • Eden C and Ackermann F (2001). SODA—The Principles. In: Rosenhead JV and Mingers J (eds). Rational Analysis for a Problematic World Revisited, 2nd edition. Wiley: Chichester, UK, pp. 21–41.

    Google Scholar 

  • Ferguson P et al (1997). Results of applying the personal software process. Computer 30: 24–31.

    Article  Google Scholar 

  • Hills PR (1971). HOCUS. P-E Group: Egham, Surrey, UK.

    Google Scholar 

  • Hodges JS (1991). Six (or so) things you can do with a bad model. Opns Res 39: 355–365.

    Article  Google Scholar 

  • Innis G and Rexstad E (1983). Simulation model simplification techniques. Simulation 41: 7–15.

    Article  Google Scholar 

  • Kotiadis K (2006). Extracting a conceptual model for a complex integrated system in healthcare. In: Garnett J, Brailsford S, Robinson S and Taylor S (eds). Proceedings of the Third Operational Research Society Simulation Workshop (SW06). The Operational Research Society: Birmingham, UK, pp. 235–245.

    Google Scholar 

  • Lehaney B and Paul RJ (1996). The use of soft systems methodology in the development of a simulation of out-patient services at Watford General Hospital. J Opl Res Soc 47: 864–870.

    Article  Google Scholar 

  • Little JDC (1994). On model building. In: Wallace W.A. (ed). Ethics in Modeling. Pergamon: New York, pp. 167–182.

    Google Scholar 

  • Morris WT (1967). On the art of modeling. Mngt Sci 13: B707–B717.

    Article  Google Scholar 

  • Nance RE (1994). The conical methodology and the evolution of simulation model development. Ann Opns Res 53: 1–45.

    Article  Google Scholar 

  • Nance RE and Overstreet CM (1987). Diagnostic assistance using digraph representation of discrete event simulation model specifications. Trans Soc Comput Simul 4: 33–57.

    Google Scholar 

  • Pidd M (1999). Just modeling through: A rough guide to modeling. Interfaces 29: 118–132.

    Article  Google Scholar 

  • Pidd M (2003). Tools for Thinking: Modelling in Management Science, 2nd edition. Wiley: Chichester, UK.

    Google Scholar 

  • Pidd M (2004). Computer Simulation in Management Science, 5th edition. Wiley: Chichester, UK.

    Google Scholar 

  • Pooley RJ (1991). Towards a standard for hierarchical process oriented discrete event diagrams. Trans Soc Comput Simul 8: 1–41.

    Google Scholar 

  • Powell SG (1995). Six key modeling heuristics. Interfaces 25: 114–125.

    Article  Google Scholar 

  • Quade ES (1988). Quantitative methods: Uses and limitations. In: Miser HJ and Quade ES (eds). Handbook of Systems Analysis: Crafts Issues and Procedures. Wiley: Chichester, UK, pp. 283–324.

    Google Scholar 

  • Richter H and März L (2000). Toward a standard process: The use of UML for designing simulation models. In: Joines JA, Barton RR, Kang K and Fishwick PA (eds). Proceedings of the 2000 Winter Simulation Conference. IEEE: Piscataway, NJ, pp. 394–398.

    Chapter  Google Scholar 

  • Robinson S (1994). Simulation projects: Building the right conceptual model. Ind Eng 26: 34–36.

    Google Scholar 

  • Robinson S (1998). Measuring service quality in the process of delivering a simulation study: The customer's perspective. Int Trans Opl Res 5: 357–374.

    Article  Google Scholar 

  • Robinson S (1999). Simulation verification validation and confidence: A tutorial. Trans Soc Comput Simul Int 16: 63–69.

    Google Scholar 

  • Robinson S (2001). Soft with a hard centre: discrete-event simulation in facilitation. J Opl Res Soc 52: 905–915.

    Article  Google Scholar 

  • Robinson S (2002). General concepts of quality for discrete-event simulation. Eur J Opl Res 138: 103–117.

    Article  Google Scholar 

  • Robinson S (2004). Simulation: The Practice of Model Development and Use. Wiley: Chichester, UK.

    Google Scholar 

  • Robinson S (2007). Conceptual modelling for simulation part I: Definition and requirements. J Opl Res Soc (doi:10.1057/palgrave.jors.2602368).

  • Robinson S and Pidd M (1998). Provider and customer expectations of successful simulation projects. J Opl Res Soc 49: 200–209.

    Article  Google Scholar 

  • Ryan J and Heavey C (2006). Requirements gathering for simulation. In: Garnett J, Brailsford S, Robinson S and Taylor S (eds). Proceedings of the Third Operational Research Society Simulation Workshop (SW06). The Operational Research Society: Birmingham, UK, pp. 175–184.

    Google Scholar 

  • Schruben LW (1983). Simulation modeling with event graphs. Commun ACM 26: 957–963.

    Article  Google Scholar 

  • Som TK and Sargent RG (1989). A formal development of event graphs as an aid to structured and efficient simulation programs. ORSA J Comput 1: 107–125.

    Article  Google Scholar 

  • Sterman JD (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin/McGraw-Hill: New York.

    Google Scholar 

  • Torn AA (1981). Simulation graphs: A general tool for modeling simulation designs. Simulation 37: 187–194.

    Article  Google Scholar 

  • van der Zee DJ (2006). Building communicative models—A job oriented approach to manufacturing simulation. In: Garnett J, Brailsford S, Robinson S and Taylor S (eds). Proceedings of the Third Operational Research Society Simulation Workshop (SW06). The Operational Research Society: Birmingham, UK, pp. 185–194.

    Google Scholar 

  • Ward SC (1989). Arguments for constructively simple models. J Opl Res Soc 40: 141–153.

    Article  Google Scholar 

  • Wild R (2002). Operations Management, 6th edition. Continuum: London.

    Google Scholar 

  • Zeigler BP (1976). Theory of Modeling and Simulation. Wiley: Chichester, UK.

    Google Scholar 

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Acknowledgements

Some sections of this paper are based on:

• Robinson S (2004). Simulation: The Practice of Model Development and Use. Wiley: Chichester, UK.

• Robinson S (2004). Designing the conceptual model in simulation studies. In: Brailsford SC, Oakshott L, Robinson S, Taylor SJE (eds). Proceedings of the 2004 Operational Research Society Simulation Workshop (SW04). Operational Research Society: Birmingham, UK, pp 259–266.

• Robinson S (2006). Issues in conceptual modelling for simulation: Setting a research agenda. In: Garnett J, Brailsford S, Robinson S, Taylor S (eds). Proceedings of the Third Operational Research Society Simulation Workshop (SW06). The Operational Research Society: Birmingham, UK, pp. 165–174.

• Robinson S (2006). Conceptual modeling for simulation: Issues and research requirements. In: Perrone LF, Wieland FP, Liu J, Lawson BE, Nicol DM and Fujimoto RM (eds). Proceedings of the 2006 Winter Simulation Conference. IEEE: Piscataway, NJ, pp 792–800.

The Ford engine plant example is used with the permission of John Ladbrook, Ford Motor Company.

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  1. University of Warwick, Coventry, UK

    S Robinson

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Robinson, S. Conceptual modelling for simulation Part II: a framework for conceptual modelling. J Oper Res Soc 59, 291–304 (2008). https://doi.org/10.1057/palgrave.jors.2602369

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