956
Views
5
CrossRef citations to date
0
Altmetric
Original Articles

Aiding the development of the conceptual model for hybrid simulation: Representing the modelling frame

Pages 2775-2793 | Received 18 Dec 2020, Accepted 06 Dec 2021, Published online: 23 Dec 2021

References

  • Andersen, D. F., & Richardson, G. P. (1997). Scripts for group model building. System Dynamics Review, 13(2), 107–129. https://doi.org/10.1002/(SICI)1099-1727(199722)13:2<107::AID-SDR120>3.0.CO;2-7
  • AnyLogic. (2019). AnyLogic: Simulation modeling software tools & solutions for business. https://www.anylogic.com/.
  • Balci, O. (1994). Validation, verification, and testing techniques throughout the life cycle of a simulation study. Annals of Operations Research, 53(1), 121–173. https://doi.org/10.1007/BF02136828
  • Bandini, S., Manzoni, S., & Vizzari, G. (2009). Agent based modeling and simulation: An informatics perspective. Journal of Artificial Societies and Social Simulation, 12(4), 4.
  • Barreteau, O., Bousquet, F., & Attonaty, J.-M. (2001). Role-playing games for opening the black box of multi-agent systems: Method and lessons of its Application to Senegal River Valley irrigated systems. Journal of Artificial Societies and Social Simulation, 4(2), 5.
  • Brailsford, S. (2014). Modeling human behaviour - An (ID)entity crisis? In Proceedings of the 2014 Winter Simulation Conference (pp. 1539–1548). IEEE.
  • Brailsford, S., Eldabi, T., Kunc, M., Mustafee, N., & Osorio, A. F. (2019). Hybrid simulation modelling in operational research: A state-of-the-art review. European Journal of Operational Research, 278(3), 721–737. https://doi.org/10.1016/j.ejor.2018.10.025
  • Brailsford, S., & Vissers, J. (2011). OR in healthcare: A European perspective. European Journal of Operational Research, 212(2), 223–234. https://doi.org/10.1016/j.ejor.2010.10.026
  • Brooks, R., & Tobias, A. (1996, August). Choosing the best model: Level of detail, complexity, and model performance. Mathematical and Computer Modelling, 24(4), 1–14. https://doi.org/10.1016/0895-7177(96)00103-3
  • Chahal, K., & Eldabi, T. (2008, December). Applicability of hybrid simulation to different modes of governance in UK healthcare. In Proceedings of the 2008 Winter Simulation Conference (pp. 1469–1477). IEEE. https://ieeexplore.ieee.org/document/4736226/
  • Checkland, P. (1989). Soft systems methodology. Human Systems Management, 8(4), 273–289. https://doi.org/10.3233/HSM-1989-8405
  • Checkland, P. (2000). Soft systems methodology: A thirty year retrospective. Systems Research and Behavioral Science, 17(S1), S11–S58. https://doi.org/10.1002/1099-1743(200011)17:1+<::AID-SRES374>3.0.CO;2-O
  • Coyle, R. G. (1997). System dynamics modelling: A practical approach. Journal of the Operational Research Society, 48(5), 544–544. https://doi.org/10.1057/palgrave.jors.2600682
  • Crooks, A., Castle, C., & Batty, M. (2008, November). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6), 417–430. https://doi.org/10.1016/j.compenvurbsys.2008.09.004
  • Davis, L. (1991). Handbook of genetic algorithms. Van Nostrand Reinhold Company.
  • Davis, J. P., & Hall, J. W. (2003). A software-supported process for assembling evidence and handling uncertainty in decision-making. Decision Support Systems, 35(3), 415–433. https://doi.org/10.1016/S0167-9236(02)00117-3
  • Davis, J., MacDonald, A., & White, L. (2010). Problem-structuring methods and project management: An example of stakeholder involvement using Hierarchical Process Modelling methodology. Journal of the Operational Research Society, 61(6), 893–904. https://doi.org/10.1057/jors.2010.12
  • Davis, J., MacDonald, A., & Marashi, S. (2007). Integrated performance measurement to support strategic decision making in engineering organisations [Paper presentation]. 2007 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 1762–1766). IEEE. http://ieeexplore.ieee.org/document/4419495/ https://doi.org/10.1109/IEEM.2007.4419495
  • Edmonds, B., & Moss, S. (2005). From KISS to KIDS – an ‘anti-simplistic’ modelling approach. In P. Davidsson, B. Logan, & K. Takadama (Eds.), Multi-agent and multi-agent-based simulation. (Vol. 3415, pp. 130–144). Springer.
  • Eldabi, T., Balaban, M., Brailsford, S., Mustafee, N., Nance, R. E., & Onggo, B. S. (2016). Hybrid simulation: Historical lessons, present challenges and futures. In Proceedings of the 2016 Winter Simulation Conference (pp. 1388–1403). IEEE. http://ieeexplore.ieee.org/document/7822192/
  • Eldabi, T., Paul, R. J., & Young, T. (2007). Simulation modelling in healthcare: Reviewing legacies and investigating futures. Journal of the Operational Research Society, 58(2), 262–270. https://doi.org/10.1057/palgrave.jors.2602222
  • Eldabi, T., Tako, A. A., Bell, D., & Tolk, A. (2019). Tutorial on means of hybrid simulation. In Proceedings of the 2019 Winter Simulation Conference (pp. 33–44). IEEE. https://ieeexplore.ieee.org/document/9004712/
  • Fone, D., Hollinghurst, S., Temple, M., Round, A., Lester, N., Weightman, A., Roberts, K., Coyle, E., Bevan, G., & Palmer, S. (2003). Systematic review of the use and value of computer simulation modelling in population health and health care delivery. Journal of Public Health Medicine, 25(4), 325–335. https://doi.org/10.1093/pubmed/fdg075
  • Forester, J. (1961). Industrial dynamics.
  • Franco, L. A., & Montibeller, G. (2010, September). Facilitated modelling in operational research. European Journal of Operational Research, 205(3), 489–500. https://doi.org/10.1016/j.ejor.2009.09.030
  • Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4), 74–94. https://doi.org/10.1287/inte.20.4.74
  • Gosavi, A. (2009, May). Reinforcement learning: A tutorial survey and recent advances. INFORMS Journal on Computing, 21(2), 178–192. https://doi.org/10.1287/ijoc.1080.0305
  • Gunal, M., & Pidd, M. (2005). Simulation modelling for performance measurement in healthcare. In Proceedings of the 2005 Winter Simulation Conference (pp. 2663–2668). IEEE. http://ieeexplore.ieee.org/document/1574567/
  • Hall, J. W., Blockley, D. I., & Davis, J. P. (1998, October). Uncertain inference using interval probability theory. International Journal of Approximate Reasoning, 19(3–4), 247–264. https://doi.org/10.1016/S0888-613X(98)10010-5
  • Helbing, D., & Molnár, P. (1995). Social force model for pedestrian dynamics. Physical Review E, 51(5), 4282–4286. https://doi.org/10.1103/PhysRevE.51.4282
  • Hwang, S., Park, M., Lee, H.-S., & Lee, S. (2016, August). Hybrid simulation framework for immediate facility restoration planning after a catastrophic disaster. Journal of Construction Engineering and Management, 142(8), 04016026. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001146
  • Jahangirian, M., Borsci, S., Shah, S. G. S., & Taylor, S. J. E. (2015). Causal factors of low stakeholder engagement: A survey of expert opinions in the context of healthcare simulation projects. SIMULATION, 91(6), 511–526. https://doi.org/10.1177/0037549715583150
  • Jones, W., Kotiadis, K., O’Hanley, J. (2019). Engaging stakeholders to extend the lifecycle of hybrid simulation models. In Proceedings of the 2019 Winter Simulation Conference (pp. 1304–1315). IEEE. https://ieeexplore.ieee.org/document/9004744/
  • Jones, W., Kotiadis, K., Paola Scaparra, M., & O’Hanley, J. (2020). Using simulation to improve the customer experience at Eurostar. Impact, 2020(1), 7–11. https://doi.org/10.1080/2058802X.2019.1703346
  • Jones, W., Willson, R. E., Sooriyabandara, M., & Doufexi, A. (2016). Wireless network MAC layer performance evaluation with full-duplex capable nodes. In Proceedings of the 12th ACM Symposium on QoS and Security for Wireless and Mobile Networks - Q2SWinet ’16 (pp. 111–118). ACM Press. Retrieved from http://dl.acm.org/citation.cfm?doid=2988272.2990294 https://doi.org/10.1145/2988272.2990294
  • Jones, W., Wilson, R. E., Doufexi, A., & Sooriyabandara, M. (2020). A pragmatic approach to clear channel assessment threshold adaptation and transmission power control for performance gain in CSMA/CA WLANs. IEEE Transactions on Mobile Computing, 19(2), 262–275. https://doi.org/10.1109/TMC.2019.2892713
  • Jones, W., Kotiadis, K., & O’Hanley, J. (2021). Developing a hybrid simulation model using both parsimonious and highly descriptive approaches: A case study from the transport industry [Paper presentation]. Proceedings of the Operational Research Society Simulation Workshop 2021. The OR Society. https://doi.org/10.36819/SW21.043
  • Jun, J. B., Jacobson, S. H., & Swisher, J. R. (1999). Application of discrete-event simulation in health care clinics: A survey. Journal of the Operational Research Society, 50(2), 109–123. https://doi.org/10.1057/palgrave.jors.2600669
  • Kotiadis, K., Tako, A. A., & Vasilakis, C. (2014). A participative and facilitative conceptual modelling framework for discrete event simulation studies in healthcare. Journal of the Operational Research Society, 65(2), 197–213. https://doi.org/10.1057/jors.2012.176
  • Law, A. M. (1991). Simulation-models level of detail determines effectiveness. Industrial Engineering, 23(10), 16.
  • Law, A. M. (2009). How to build valid and credible simulation models. In Proceedings of the 2009 Winter Simulation Conference (pp. 24–33). IEEE. http://ieeexplore.ieee.org/document/5429312/
  • Linnéusson, G., Ng, A. H., & Aslam, T. (2020, March). A hybrid simulation-based optimization framework supporting strategic maintenance development to improve production performance. European Journal of Operational Research, 281(2), 402–414. https://doi.org/10.1016/j.ejor.2019.08.036
  • Lowery, J., Hakes, B., Lilegdon, W., Keller, L., Mabrouk, K., & McGuire, F. (1994). Barriers to implementing simulation in health care [Paper presentation]. Proceedings of the 1994 Winter Simulation Conference (pp. 868–875). IEEE. http://ieeexplore.ieee.org/document/717447/ https://doi.org/10.1109/WSC.1994.717447
  • Lynch, C., Padilla, J., Diallo, S., Sokolowski, J., & Banks, C. (2014, December). A multi-paradigm modeling framework for modeling and simulating problem situations. In Proceedings of the 2014 Winter Simulation Conference (pp. 1688–1699). IEEE. http://ieeexplore.ieee.org/document/7020019/
  • Marashi, E., & Davis, J. P. (2007). A systems-based approach for supporting discourse in decision making. Computer-Aided Civil and Infrastructure Engineering, 22(7), 511–526. https://doi.org/10.1111/j.1467-8667.2007.00507.x
  • Matos, J. F., Houston, S., Blum, W., & Carreira, S. P. (2001). Modelling and mathematics education: ICTMA 9-Applications in science and technology. Elsevier.
  • Mingers, J. (2011). Soft OR comes of age—but not everywhere! Omega, 39(6), 729–741. https://doi.org/10.1016/j.omega.2011.01.005
  • Monks, T., Currie, C. S. M., Onggo, B. S., Robinson, S., Kunc, M., & Taylor, S. J. E. (2019). Strengthening the reporting of empirical simulation studies: Introducing the stress guidelines. Journal of Simulation, 13(1), 55–67. https://doi.org/10.1080/17477778.2018.1442155
  • Monks, T., Robinson, S., & Kotiadis, K. (2009, December). Model reuse versus model development: Effects on credibility and learning. In Proceedings of the 2009 Winter Simulation Conference (Vol. 2010, pp. 767–778). IEEE. http://ieeexplore.ieee.org/document/5429691/
  • Morgan, J. S., Howick, S., & Belton, V. (2017, March). A toolkit of designs for mixing discrete event simulation and system dynamics. European Journal of Operational Research, 257(3), 907–918. https://doi.org/10.1016/j.ejor.2016.08.016
  • Mustafee, N., Alstad, A., Larsen, B., E Taylor, S., & Ladbrook, J. (2006). Grid-enabling FIRST: speeding up simulation applications using WinGrid [Paper presentation]. 2006 Tenth IEEE International Symposium on Distributed Simulation and Real-Time Applications (pp. 157–164). IEEE. http://ieeexplore.ieee.org/document/4020799/ https://doi.org/10.1109/DS-RT.2006.19
  • Mustafee, N., Brailsford, S., Djanatliev, A., Eldabi, T., Kunc, M., & Tolk, A. (2017). Purpose and benefits of hybrid simulation: Contributing to the convergence of its definition [Paper presentation]. Proceedings of 2017 Winter Simulation Conference (pp. 1–15). IEEE. http://dl.acm.org/citation.cfm?id=3242181.3242315
  • Mustafee, N., Harper, A., & Onggo, B. S. (2020, December). Hybrid modelling and simulation (MS): Driving innovation in the theory and practice of MS [Paper presentation]. 2020 Winter Simulation Conference (WSC) (Vol. 2020, pp. 3140–3151). IEEE. https://ieeexplore.ieee.org/document/9383892/ https://doi.org/10.1109/WSC48552.2020.9383892
  • Nasirzadeh, F., Khanzadi, M., & Mir, M. (2018, March). A hybrid simulation framework for modelling construction projects using agent-based modelling and system dynamics: An application to model construction workers’ safety behavior. International Journal of Construction Management, 18(2), 132–143. https://doi.org/10.1080/15623599.2017.1285485
  • Oscarsson, J., & Moris, M. U. (2002). Documentation of discrete event simulation models for manufacturing system life cycle simulation [Paper presentation]. Proceedings of the 2002 Winter Simulation Conference (Vol. 2, pp. 1073–1078). https://doi.org/10.1109/WSC.2002.1166359
  • Peck, S. (1998, July). Group model building: Facilitating team learning using system dynamics. Journal of the Operational Research Society, 49(7), 766–767. https://www.tandfonline.com/doi/full/10.1057/palgrave.jors.2600567 https://doi.org/10.1057/palgrave.jors.2600567
  • Powell, J., & Mustafee, N. (2014, December). Soft OR approaches in problem formulation stage of a hybrid M&S study. In Proceedings of the Winter Simulation Conference 2014 (pp. 1664––1675). IEEE. http://ieeexplore.ieee.org/document/7020017/
  • Powell, J., & Mustafee, N. (2017, October). Widening requirements capture with soft methods: An investigation of hybrid MS studies in health care. Journal of the Operational Research Society, 68(10), 1211–1222. https://doi.org/10.1057/s41274-016-0147-6
  • Rausch, T., Hummer, W., & Muthusamy, V. (2020, June). PipeSim: Trace-driven simulation of large-scale AI operations platforms. http://arxiv.org/abs/2006.12587
  • Robinson, S. (2004). Simulation: The practice of model development and use. John Wiley & Sons.
  • Robinson, S. (2007). The future’s bright the future’s…conceptual modelling for simulation! Journal of Simulation, 1(3), 149–152. https://doi.org/10.1057/palgrave.jos.4250026
  • Robinson, S. (2008a, March 1). Conceptual modelling for simulation part i: Definition and requirements. Journal of the Operational Research Society, 59(3), 278–290. https://doi.org/10.1057/palgrave.jors.2602368
  • Robinson, S. (2008b). Conceptual modelling for simulation Part II: A framework for conceptual modelling. Journal of the Operational Research Society, 59(3), 291–304. https://doi.org/10.1057/palgrave.jors.2602369
  • Robinson, S. (2013). Conceptual modeling for simulation. In Proceedings of the 2013 Winter Simulation Conference (pp. 377–388). IEEE. http://ieeexplore.ieee.org/document/6721435/
  • Robinson, S. (2020). Conceptual modelling for simulation: Progress and grand challenges. Journal of Simulation, 14(1), 1–20. https://doi.org/10.1080/17477778.2019.1604466
  • Robinson, S., Arbez, G., Birta, L. G., Tolk, A., & Wagner, G. (2015). Conceptual modeling: Definition, purpose and benefits [Paper presentation]. 2015 Winter Simulation Conference (WSC), (pp. 2812–2826). https://doi.org/10.1109/WSC.2015.7408386
  • Robinson, S., Radnor, Z. J., Burgess, N., & Worthington, C. (2012). Simlean: Utilising simulation in the implementation of lean in healthcare. European Journal of Operational Research, 219(1), 188–197. https://doi.org/10.1016/j.ejor.2011.12.029
  • Robinson, S., Worthington, C., Burgess, N., & Radnor, Z. J. (2014). Facilitated modelling with discrete-event simulation: Reality or myth? European Journal of Operational Research, 234(1), 231–240. https://doi.org/10.1016/j.ejor.2012.12.024
  • Rouwette, E. A., Vennix, J. A., & Van Mullekom, T. (2002, March). Group model building effectiveness: A review of assessment studies. System Dynamics Review, 18(1), 5–45. https://doi.org/10.1002/sdr.229
  • Shanthikumar, J. G., & Sargent, R. G. (1983). A unifying view of hybrid simulation/analytic models and modeling. Operations Research, 31(6), 1030–1052. https://doi.org/10.1287/opre.31.6.1030
  • Sherman, S. W., & Browne, J. C. (1973). Trace driven modeling: Review and overview. In Proceedings of the 1st Symposium on Simulation of Computer Systems (pp. 200–207). IEEE.
  • Siokou, C., Morgan, R., & Shiell, A. (2014, November). Group model building: A participatory approach to understanding and acting on systems. Public Health Research and Practice, 25(1), e2511404.
  • Tako, A. A., & Kotiadis, K. (2015). PartiSim: A multi-methodology framework to support facilitated simulation modelling in healthcare. European Journal of Operational Research, 244(2), 555–564. https://doi.org/10.1016/j.ejor.2015.01.046
  • Tako, A. A., Eldabi, T., Fishwick, P., Krejci, C. C., & Kunc, M. (2019). Panel - towards conceptual modeling for hybrid simulation: Setting the scene [Paper presentation]. 2019 Winter Simulation Conference (WSC) (pp. 1267–1279). https://doi.org/10.1109/WSC40007.2019.9004838
  • Tolk, A., Harper, A., & Mustafee, N. (2021). Hybrid models as transdisciplinary research enablers. European Journal of Operational Research, 291(3), 1075–1090. https://linkinghub.elsevier.com/retrieve/pii/S0377221720308869
  • Triebig, C., & Klügl, F. (2009, July). Elements of a documentation framework for agent-based simulation models. Cybernetics and Systems, 40(5), 441–474. https://doi.org/10.1080/01969720902922459
  • Vandekerckhove, J., Matzke, D., & Wagenmakers, E.-J. (2015). Model comparison and the principle of parsimony (Vol. 1; J. R. Busemeyer, Z. Wang, J. T. Townsend, & A. Eidels, Eds.). Oxford University Press.
  • Venkateswaran, J., & Son, Y. J. (2005, October). Hybrid system dynamic - discrete event simulation-based architecture for hierarchical production planning. International Journal of Production Research, 43(20), 4397–4429. https://doi.org/10.1080/00207540500142472
  • Vennix, J. A. M. (1999). Group model-building: Tackling messy problems. System Dynamics Review, 15(4), 379–401. https://doi.org/10.1002/(SICI)1099-1727(199924)15:4<379::AID-SDR179>3.0.CO;2-E
  • Willemain, T. R. (1995). Model formulation: What experts think about and when. Operations Research, 43(6), 916–932. https://doi.org/10.1287/opre.43.6.916
  • Wilson, J. C. T. (1981). Implementation of computer simulation projects in health care. The Journal of the Operational Research Society, 32(9), 825–832. https://doi.org/10.1057/jors.1981.161
  • Young, T., Eatock, J., Jahangirian, M., Naseer, A., & Lilford, R. (2009). Three critical challenges for modeling and simulation in healthcare. In Proceedings of the 2009 Winter Simulation Conference (pp. 1823–1830). IEEE. http://dl.acm.org/citation.cfm?id=1995456.1995709
  • Zulkepli, J., & Eldabi, T. (2015, December). Towards a framework for conceptual model hybridization in healthcare. In Proceedings of the 2015 Winter Simulation Conference (pp. 1597–1608). IEEE. http://ieeexplore.ieee.org/document/7408280/

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.