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Conceptual modelling for simulation Part I: definition and requirements

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Journal of the Operational Research Society

Abstract

Conceptual modelling is probably the most important aspect of a simulation study. It is also the most difficult and least understood. Over 40 years of simulation research and practice have provided only limited information on how to go about designing a simulation conceptual model. This paper, the first of two, discusses the meaning of conceptual modelling and the requirements of a conceptual model. Founded on existing literature, a definition of a conceptual model is provided. Four requirements of a conceptual model are described: validity, credibility, utility and feasibility. The need to develop the simplest model possible is also discussed. Owing to a paucity of advice on how to design a conceptual model, the need for a conceptual modelling framework is proposed. Built on the foundations laid in this paper, a conceptual modelling framework is described in the paper that follows.

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References

  • Balci O (1994). Validation, verification, and testing techniques throughout the life cycle of a simulation study. Ann Opns Res 53: 121–173.

    Article  Google Scholar 

  • Balci O (2001). A methodology for certification of modeling and simulation applications. ACM Trans Model Comput Simul 11: 352–377.

    Article  Google Scholar 

  • Balci O and Nance RE (1985). Formulated problem verification as an explicit requirement of model credibility. Simulation 45(2): 76–86.

    Article  Google Scholar 

  • Bankes S (1993). Exploratory modeling for policy analysis. Opns Res 41: 435–449.

    Article  Google Scholar 

  • Borah JJ (2002). Conceptual modeling—The missing link of simulation development. Proceedings of the 2002 Spring Simulation Interoperability Workshop. Available online.

  • Brooks RJ and Tobias AM (1996a). Choosing the best model: Level of detail, complexity and model performance. Math Comput Model 24(4): 1–14.

    Article  Google Scholar 

  • Brooks RJ and Tobias AM (1996b). A framework for choosing the best model structure in mathematical and computer modeling. Proceedings of the 6th Annual Conference AI, Simulation, and Planning in High Autonomy Systems, University of Arizona, pp 53–60.

  • Carson JS (1986). Convincing users of model's validity is challenging aspect of modeler's job. Ind Eng 18(6): 74–85.

    Google Scholar 

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

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Cochran JK, Mackulak GT and Savory PA (1995). Simulation project characteristics in industrial settings. Interfaces 25: 104–113.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Davies R, Roderick P and Raftery J (2003). The evaluation of disease prevention and treatment using simulation models. Eur J Opl Res 150: 53–66.

    Article  Google Scholar 

  • DMSO (2005). Defence modeling and simulation office, HLA Page. Available online.

  • Evans JR (1992). Creativity in MS/OR: Improving problem solving through creative thinking. Interfaces 22(2): 87–91.

    Article  Google Scholar 

  • Ferguson P et al (1997). Results of applying the personal software process. Computer 5: 24–31.

    Article  Google Scholar 

  • Fishwick PA (1995). Simulation Model Design and Execution: Building Digital Worlds. Prentice-Hall: Upper Saddle River, NJ.

    Google Scholar 

  • Gass SI and Joel LS (1981). Concepts of model confidence. Comput Opns Res 8: 341–346.

    Article  Google Scholar 

  • Guru A and Savory P (2004). A template-based conceptual modeling infrastructure for simulation of physical security systems. In: Ingalls RG, Rossetti MD, Smith JS and Peters BA (eds). Proceedings of the 2004 Winter Simulation Conference. IEEE: Piscataway, NJ, pp. 866–873.

    Google Scholar 

  • Haddix F (2001). Conceptual modeling revisited: A developmental model approach for modeling and simulation. Proceedings of the 2001 Fall Simulation Interoperability Workshop. Available online.

  • Henriksen JO (1988). One system, several perspectives, many models. In: Abrams M, Haigh P and Comfort J (eds). Proceedings of the 1988 Winter Simulation Conference. IEEE: Piscataway, NJ, pp. 352–356.

    Google Scholar 

  • Henriksen JO (1989). Alternative modeling perspectives: Finding the creative spark. In: MacNair EA, Musselman KJ and Heidelberger P (eds). Proceedings of the 1989 Winter Simulation Conference. IEE: Piscataway, NJ, pp. 648–652.

    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(1): 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 

  • Lacy LW et al (2001). Developing a consensus perspective on conceptual models for simulation systems. Proceedings of the 2001 Spring Simulation Interoperability Workshop. Available online.

  • Law AM (1991). Simulation model's level of detail determines effectiveness. Ind Eng 23(10): 16–18.

    Google Scholar 

  • Law AM and Kelton WD (2000). Simulation Modeling and Analysis, 3rd edition. McGraw-Hill: New York.

    Google Scholar 

  • Lucas TW and McGunnigle JE (2003). When is model complexity too much? Illustrating the benefits of simple models with Hughes' salvo equations. Naval Res Logist 50: 197–217.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Musselman KJ (1992). Conducting a successful simulation project. In: Swain JJ, Goldsman D, Crain RC and Wilson JR (eds). Proceedings of the 1992 Winter Simulation Conference. IEEE: Piscataway, NJ, pp. 115–121.

    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 Arthur JD (2006). Software requirements engineering: Exploring the role in simulation model development. 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. 117–127.

    Google Scholar 

  • Nydick RL, Liberatore MJ and Chung QB (2002). Modeling by elaboration: An application to visual process simulation. INFOR 40: 347–361.

    Google Scholar 

  • Ören TI (1981). Concepts and criteria to assess acceptability of simulation studies: A frame of reference. Commun ACM 28: 190–201.

    Google Scholar 

  • Ören TI (1984). Quality assurance in modeling and simulation: A taxonomy. In: Ören TI, Zeigler BP and Elzas MS (eds). Simulation and Model-based Methodologies: An Integrative Approach. Springer-Verlag: Heidelberg, Germany, pp. 477–517.

    Chapter  Google Scholar 

  • Pace DK (1999). Development and documentation of a simulation conceptual model. Proceedings of the 1999 Fall Simulation Interoperability Workshop. Available online.

  • Pace DK (2000a). Simulation conceptual model development. Proceedings of the 2000 Spring Simulation Interoperability Workshop. Available online.

  • Pace DK (2000b). Ideas about simulation conceptual model development. Johns Hopkins APL Tech Dig 21: 327–336.

    Google Scholar 

  • Pace DK (2002). The value of a quality simulation conceptual model. Model Simul Mag 1(1): 9–10.

    Google Scholar 

  • Pace DK (2003). Thoughts about the simulation conceptual model. Proceedings of the 2003 Spring Simulation Interoperability Workshop. Available online.

  • 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 

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

    Article  Google Scholar 

  • Pritsker AAB (1986). Model evolution: A rotary table case history. In: Wilson J, Henriksen J and Roberts S (eds). Proceedings of the 1986 Winter Simulation Conference. IEEE: Piscataway, NJ, pp. 703–707.

    Google Scholar 

  • Pritsker AAB (1987). Model evolution II: An FMS design problem. In: Thesen A, Grant H and Kelton WD (eds). Proceedings of the 1987 Winter Simulation Conference. IEEE: Piscataway, NJ, pp. 567–574.

    Google Scholar 

  • Pritsker AAB (1998). Principles of simulation modeling. In: Banks J. (ed). Handbook of Simulation. Wiley: New York, pp. 31–51.

    Chapter  Google Scholar 

  • Robinson S (1994). Simulation projects: Building the right conceptual model. Ind Eng 26(9): 34–36.

    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). Modes of simulation practice: Approaches to business and military simulation. Simul Pract Theory 10: 513–523.

    Article  Google Scholar 

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

    Google Scholar 

  • Robinson S (2005). Distributed simulation and simulation practice. Simulation: Trans Soc Model Comput Simul 81: 5–13.

    Article  Google Scholar 

  • Robinson S (2007). Conceptual modelling for simulation Part II: A framework for conceptual modelling. J Opl Res Soc, doi:10.1057/palgrave.jors.2602369.

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

    Article  Google Scholar 

  • 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 

  • Schmeiser BW (2001). Some myths and common errors in simulation experiments. In: Peters BA, Smith JS, Medeiros DJ and Rohrer MW (eds). Proceedings of the 2001 Winter Simulation Conference. IEEE: Piscataway, NJ, pp. 39–46.

    Chapter  Google Scholar 

  • Schruben L and Yücesan E (1993). Complexity of simulation models: A graph theoretic approach. In: Evans GW, Mollaghasemi M, Russell EC and Biles WE (eds). Proceedings of the 1993 Winter Simulation Conference. IEEE: Piscataway, NJ, pp. 641–649.

    Chapter  Google Scholar 

  • Sevinc S (1990). Automation of simplification in discrete event modeling and simulation. Int J Gen Syst 18: 125–142.

    Article  Google Scholar 

  • Shannon RE (1975). Systems simulation: The Art and Science. Prentice-Hall: Englewood Cliffs, NJ.

    Google Scholar 

  • Teeuw WB and van den Berg H (1997). On the quality of conceptual models. In: Liddle SW (ed). Proceedings of the ER'97 Workshop on Behavioral Models and Design Transformations: Issues and Opportunities in Conceptual Modeling. Available online.

  • Thomas A and Charpentier P (2005). Reducing simulation models for scheduling manufacturing facilities. Eur J Opl Res 161: 111–125.

    Article  Google Scholar 

  • van der Zee DJ and van der Vorst JGAJ (2005). A modeling framework for supply chain simulation: Opportunities for improved decision making. Decis Sci 36: 65–95.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Webster DB, Padgett ML, Hines GS and Sirois DL (1984). Determining the level of detail in a simulation model—A case study. Comput Ind Eng 8: 215–225.

    Article  Google Scholar 

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

    Google Scholar 

  • Willemain TR (1994). Insights on modeling from a dozen experts. Opns Res 42: 213–222.

    Article  Google Scholar 

  • Willemain TR (1995). Model formulation: What experts think about and when. Opns Res 43: 916–932.

    Article  Google Scholar 

  • Yin HY and Zhou ZN (1989). Simplification techniques of simulation models. Proceedings of Beijing International Conference on System Simulation and Scientific Computing, IEEE: Piscataway: NJ, pp 782–786.

  • Zeigler BP (1976). Theory of Modeling and Simulation. Wiley: New York.

    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 and 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 and 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 BG, 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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    S Robinson

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Robinson, S. Conceptual modelling for simulation Part I: definition and requirements. J Oper Res Soc 59, 278–290 (2008). https://doi.org/10.1057/palgrave.jors.2602368

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