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Modeling Logistic Performance in Quantitative Microbial Risk Assessment

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  • Hajo Rijgersberg
  • Seth Tromp
  • Liesbeth Jacxsens
  • Mieke Uyttendaele

Abstract

In quantitative microbial risk assessment (QMRA), food safety in the food chain is modeled and simulated. In general, prevalences, concentrations, and numbers of microorganisms in media are investigated in the different steps from farm to fork. The underlying rates and conditions (such as storage times, temperatures, gas conditions, and their distributions) are determined. However, the logistic chain with its queues (storages, shelves) and mechanisms for ordering products is usually not taken into account. As a consequence, storage times—mutually dependent in successive steps in the chain—cannot be described adequately. This may have a great impact on the tails of risk distributions. Because food safety risks are generally very small, it is crucial to model the tails of (underlying) distributions as accurately as possible. Logistic performance can be modeled by describing the underlying planning and scheduling mechanisms in discrete‐event modeling. This is common practice in operations research, specifically in supply chain management. In this article, we present the application of discrete‐event modeling in the context of a QMRA for Listeria monocytogenes in fresh‐cut iceberg lettuce. We show the potential value of discrete‐event modeling in QMRA by calculating logistic interventions (modifications in the logistic chain) and determining their significance with respect to food safety.

Suggested Citation

  • Hajo Rijgersberg & Seth Tromp & Liesbeth Jacxsens & Mieke Uyttendaele, 2010. "Modeling Logistic Performance in Quantitative Microbial Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 30(1), pages 20-31, January.
    • Handle: RePEc:wly:riskan:v:30:y:2010:i:1:p:20-31
    DOI: 10.1111/j.1539-6924.2009.01338.x
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    References listed on IDEAS

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    1. Hong Yang & Amirhossein Mokhtari & Lee‐Ann Jaykus & Roberta A. Morales & Sheryl C. Cates & Peter Cowen, 2006. "Consumer Phase Risk Assessment for Listeria monocytogenes in Deli Meats," Risk Analysis, John Wiley & Sons, vol. 26(1), pages 89-103, February.
    2. K. Francois & F. Devlieghere & M. Uyttendaele & J. Debevere, 2006. "Risk Assessment of Listeria monocytogenes: Impact of Individual Cell Variability on the Exposure Assessment Step," Risk Analysis, John Wiley & Sons, vol. 26(1), pages 105-114, February.
    3. Moez Sanaa & Louis Coroller & Olivier Cerf, 2004. "Risk Assessment of Listeriosis Linked to the Consumption of Two Soft Cheeses Made from Raw Milk: Camembert of Normandy and Brie of Meaux," Risk Analysis, John Wiley & Sons, vol. 24(2), pages 389-399, April.
    4. Arnout R. Standaert & Kjell Francois & Frank Devlieghere & Johan Debevere & Jan F. Van Impe & Annemie H. Geeraerd, 2007. "Modeling Individual Cell Lag Time Distributions for Listeria monocytogenes," Risk Analysis, John Wiley & Sons, vol. 27(1), pages 241-254, February.
    5. Isabelle Albert & Régis Pouillot & Jean‐Baptiste Denis, 2005. "Stochastically Modeling Listeria Monocytogenes Growth in Farm Tank Milk," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1171-1185, October.
    6. Thomas Ross & Thomas Alexander McMeekin, 2003. "Modeling Microbial Growth Within Food Safety Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 23(1), pages 179-197, February.
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    1. Janssen, Larissa & Diabat, Ali & Sauer, Jürgen & Herrmann, Frank, 2018. "A stochastic micro-periodic age-based inventory replenishment policy for perishable goods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 445-465.

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