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Dynamic freight flow modelling for risk evaluation in food supply

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  • Balster, Andreas
  • Friedrich, Hanno

Abstract

This paper presents a calibrated dynamic multi-scale multi-regional input–output (MSMRIO) model of the German food supply system based on real data. The model comprises 51 commodity groups from farm to fork differentiating three different temperature ranges as well as living animals. Spatially, it works on an aggregate level of 402 regions within Germany as well as its 50 most important trading nations. It determines the commodity flows and the additionally needed transport capacity in case of disruptions. Showing how changes in production, inventories, sourcing, and consumption affect commodity flows, the model uncovers vulnerabilities and makes risk evaluation possible.

Suggested Citation

  • Balster, Andreas & Friedrich, Hanno, 2019. "Dynamic freight flow modelling for risk evaluation in food supply," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 121(C), pages 4-22.
    • Handle: RePEc:eee:transe:v:121:y:2019:i:c:p:4-22
    DOI: 10.1016/j.tre.2018.03.002
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    Cited by:

    1. Tim Schlaich & Abigail L. Horn & Marcel Fuhrmann & Hanno Friedrich, 2020. "A Gravity-Based Food Flow Model to Identify the Source of Foodborne Disease Outbreaks," IJERPH, MDPI, vol. 17(2), pages 1-20, January.
    2. Li, Jianbin & Zheng, Yuting & Dai, Bin & Yu, Jiang, 2020. "Implications of matching and pricing strategies for multiple-delivery-points service in a freight O2O platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    3. Shaojian Qu & Hao Cai & Dandan Xu & Nabé Mohamed, 2021. "Uncertainty in the prediction and management of CO2 emissions: a robust minimum entropy approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2419-2438, July.
    4. Heiner Ackermann & Erik Diessel & Michael Helmling & Neil Jami & Johanna Münch, 2024. "Computing Optimal Mitigation Plans for Force-Majeure Scenarios in Dynamic Manufacturing Chains," SN Operations Research Forum, Springer, vol. 5(2), pages 1-35, June.
    5. Tavasszy, Lóránt A., 2020. "Predicting the effects of logistics innovations on freight systems: Directions for research," Transport Policy, Elsevier, vol. 86(C), pages 1-6.

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