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Using soft OR principles for collaborative simulation: a case study in the Dutch airline industry

Author

Listed:
  • M den Hengst

    (Netherlands Organization for Applied Scientific Research, Work and Employment Unit
    Delft University of Technology)

  • G-J de Vreede

    (Delft University of Technology
    University of Nebraska at Omaha)

  • R Maghnouji

    (Delft University of Technology)

Abstract

Collaborative simulation is a method to handle problem situations that are technologically and socially complex. It combines discrete event simulation with methods, techniques and tools designed to help groups progress with their problems. Discrete event simulation and group support both appear to have clear promises in embedding soft OR principles, yet also contain limitations. Furthermore, simulation and group support appear to be complementary, balancing each other's limitations. Therefore, the combination of group support and discrete event simulation seems to be a fruitful vehicle for the application of soft OR principles. Through experiences in a case study at the cargo department of a major airline, we examine how collaborative simulation can be applied in practice as problem structuring method. We conclude that collaborative simulation has definite potential as a soft OR technique, but further research is required to speed up different steps of simulation to fully use simulation in interactive decision making sessions with multiple stakeholders.

Suggested Citation

  • M den Hengst & G-J de Vreede & R Maghnouji, 2007. "Using soft OR principles for collaborative simulation: a case study in the Dutch airline industry," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(5), pages 669-682, May.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:5:d:10.1057_palgrave.jors.2602353
    DOI: 10.1057/palgrave.jors.2602353
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    References listed on IDEAS

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    Cited by:

    1. Robinson, Stewart & Kotiadis, Kathy, 2016. "Can involving clients in simulation studies help them solve their future problems? A transfer of learning experimentAuthor-Name: Monks, Thomas," European Journal of Operational Research, Elsevier, vol. 249(3), pages 919-930.
    2. Gogi, Anastasia & Tako, Antuela A. & Robinson, Stewart, 2016. "An experimental investigation into the role of simulation models in generating insights," European Journal of Operational Research, Elsevier, vol. 249(3), pages 931-944.
    3. Kotiadis, K. & Tako, A.A., 2018. "Facilitated post-model coding in discrete event simulation (DES): A case study in healthcare," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1120-1133.
    4. Giovanni Ferri & Angelo Leogrande, 2015. "Was the Crisis due to a shift from stakeholder to shareholder finance? Surveying the debate," Mo.Fi.R. Working Papers 108, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    5. Harper, Alison & Mustafee, Navonil & Yearworth, Mike, 2021. "Facets of trust in simulation studies," European Journal of Operational Research, Elsevier, vol. 289(1), pages 197-213.
    6. Proudlove, N.C. & Bisogno, S. & Onggo, B.S.S. & Calabrese, A. & Levialdi Ghiron, N., 2017. "Towards fully-facilitated discrete event simulation modelling: Addressing the model coding stage," European Journal of Operational Research, Elsevier, vol. 263(2), pages 583-595.
    7. Robinson, Stewart & Worthington, Claire & Burgess, Nicola & Radnor, Zoe J., 2014. "Facilitated modelling with discrete-event simulation: Reality or myth?," European Journal of Operational Research, Elsevier, vol. 234(1), pages 231-240.
    8. D Champion & J M Wilson, 2010. "The impact of contingency factors on validation of problem structuring methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(9), pages 1420-1431, September.

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