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Comparing conventional and distributed approaches to simulation in a complex supply-chain health system

Author

Listed:
  • K Katsaliaki

    (Middlesex University)

  • N Mustafee

    (Brunel University)

  • S J E Taylor

    (Brunel University)

  • S Brailsford

    (University of Southampton)

Abstract

Decision making in modern supply chains can be extremely daunting due to their complex nature. Discrete-event simulation is a technique that can support decision making by providing what-if analysis and evaluation of quantitative data. However, modelling supply chain systems can result in massively large and complicated models that can take a very long time to run even with today's powerful desktop computers. Distributed simulation has been suggested as a possible solution to this problem, by enabling the use of multiple computers to run models. To investigate this claim, this paper presents experiences in implementing a simulation model with a ‘conventional’ approach and with a distributed approach. This study takes place in a healthcare setting, the supply chain of blood from donor to recipient. The study compares conventional and distributed model execution times of a supply chain model simulated in the simulation package Simul8. The results show that the execution time of the conventional approach increases almost linearly with the size of the system and also the simulation run period. However, the distributed approach to this problem follows a more linear distribution of the execution time in terms of system size and run time and appears to offer a practical alternative. On the basis of this, the paper concludes that distributed simulation can be successfully applied in certain situations.

Suggested Citation

  • K Katsaliaki & N Mustafee & S J E Taylor & S Brailsford, 2009. "Comparing conventional and distributed approaches to simulation in a complex supply-chain health system," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 43-51, January.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:1:d:10.1057_palgrave.jors.2602531
    DOI: 10.1057/palgrave.jors.2602531
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    References listed on IDEAS

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    1. S Robinson, 2005. "Discrete-event simulation: from the pioneers to the present, what next?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(6), pages 619-629, June.
    2. K Katsaliaki & S C Brailsford, 2007. "Using simulation to improve the blood supply chain," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 219-227, February.
    3. Goyal, S. K. & Giri, B. C., 2001. "Recent trends in modeling of deteriorating inventory," European Journal of Operational Research, Elsevier, vol. 134(1), pages 1-16, October.
    4. van Donselaar, K. & van Woensel, T. & Broekmeulen, R. & Fransoo, J., 2006. "Inventory control of perishables in supermarkets," International Journal of Production Economics, Elsevier, vol. 104(2), pages 462-472, December.
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    Cited by:

    1. Taylor, Simon J.E., 2019. "Distributed simulation: state-of-the-art and potential for operational research," European Journal of Operational Research, Elsevier, vol. 273(1), pages 1-19.
    2. Joana Cunha & Vasco Reis & Paulo Teixeira, 2022. "Development of an agent-based model for railway infrastructure project appraisal," Transportation, Springer, vol. 49(6), pages 1649-1681, December.
    3. Jesús Isaac Vázquez-Serrano & Rodrigo E. Peimbert-García & Leopoldo Eduardo Cárdenas-Barrón, 2021. "Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review," IJERPH, MDPI, vol. 18(22), pages 1-20, November.

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