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A flexible and generic approach to dynamic modelling of supply chains

Author

Listed:
  • W Y Hung

    (Imperial College)

  • S Kucherenko

    (Imperial College)

  • N J Samsatli

    (Imperial College)

  • N Shah

    (Imperial College)

Abstract

In this paper, we present a new modelling approach for realistic supply chain simulation. The model provides an experimental environment for informed comparison between different supply chain policies. A basic simulation model for a generic node, from which a supply chain network can be built, has been developed using an object-oriented approach. This generic model allows the incorporation of the information and physical systems and decision-making policies used by each node. The object-oriented approach gives the flexibility in specifying the supply chain configuration and operation decisions, and policies. Stochastic simulations are achieved by applying Latin Supercube Sampling to the uncertain variables in descending order of importance, which reduces the number of simulations required. We also present a case study to show that the model is applicable to a real-life situation for dynamic stochastic studies.

Suggested Citation

  • W Y Hung & S Kucherenko & N J Samsatli & N Shah, 2004. "A flexible and generic approach to dynamic modelling of supply chains," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(8), pages 801-813, August.
  • Handle: RePEc:pal:jorsoc:v:55:y:2004:i:8:d:10.1057_palgrave.jors.2601740
    DOI: 10.1057/palgrave.jors.2601740
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    References listed on IDEAS

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    Cited by:

    1. Martínez-Olvera, César, 2009. "Benefits of using hybrid business models within a supply chain," International Journal of Production Economics, Elsevier, vol. 120(2), pages 501-511, August.
    2. Awudu, Iddrisu & Zhang, Jun, 2012. "Uncertainties and sustainability concepts in biofuel supply chain management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1359-1368.
    3. Niels Bugert & Rainer Lasch, 2023. "Analyzing upstream and downstream risk propagation in supply networks by combining Agent-based Modeling and Bayesian networks," Journal of Business Economics, Springer, vol. 93(5), pages 859-889, July.

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