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Multi-product multi-region supply chain optimisation for seasonal crops

Author

Listed:
  • Harry Sisley
  • Guvenc Dik
  • James McGree
  • Paul Corry

Abstract

There is a large demand for seasonal crops year-round within Australia, even when they are considered out of season. The demand is satisfied by continually moving production throughout the year to climates where the crops are in season. Management of the supply chain for a major national grower is challenging for several reasons. Due to the large number of planting decisions, it is standard practice for a team of production planners to create the annual production plan. For fresh food production, the supply chain does not contain intermediate storage requiring that the production plan is carefully timed not to waste resources with overproduction. In this paper, we develop a supply chain model that simultaneously manages the production of multiple crops across many growing regions. Production is set to satisfy the demand of multiple end-products while considering the packing plants' throughput capacity, each growing region's harvest capacity, and farm capacity. A time delay may be applied when moving between stages of the supply chain due to the geographic scale being modelled. A deterministic Mixed Integer Program is used to find the optimal planting plan, which minimises the deviation from demand for all products year-round at a minimal cost. Due to the excessive runtime for solving the model, a heuristic solution method is introduced. Numerical experiments demonstrate the advantage of the proposed model over the current manual planning process, which can solve the problem faster and with less deviation across the planning horizon.

Suggested Citation

  • Harry Sisley & Guvenc Dik & James McGree & Paul Corry, 2023. "Multi-product multi-region supply chain optimisation for seasonal crops," International Journal of Production Research, Taylor & Francis Journals, vol. 61(16), pages 5704-5722, August.
  • Handle: RePEc:taf:tprsxx:v:61:y:2023:i:16:p:5704-5722
    DOI: 10.1080/00207543.2022.2112105
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