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Australian Sugar Mills Optimize Harvester Rosters to Improve Production

Author

Listed:
  • Andrew J. Higgins

    (CRC for Sustainable Sugar Production, CSIRO Sustainable Ecosystems, 120 Meiers Road, Indooroopilly 4068, Australia)

Abstract

Increasing cost/price ratios in sugarcane production and the pressure to remain internationally competitive have forced Australian sugar mills to try to use their infrastructure more efficiently. Generating rosters for sugarcane harvesters manually is difficult because the mills have a large number of harvesters and tight capacities in the transportation facilities. The Cooperative Research Centre for Sustainable Sugar Production conducted a participatory research process with five mills in the Australian sugar industry to develop models to optimize harvester rosters. Embedded in the research process and underpinned by action learning was the development of a novel integer-programming model, its validation, and its implementation. The participatory research overcame barriers to implementation of the rosters produced by the model and allowed the five participating mills to realize benefits in terms of more efficient transport operations.

Suggested Citation

  • Andrew J. Higgins, 2002. "Australian Sugar Mills Optimize Harvester Rosters to Improve Production," Interfaces, INFORMS, vol. 32(3), pages 15-25, June.
    • Handle: RePEc:inm:orinte:v:32:y:2002:i:3:p:15-25
    DOI: 10.1287/inte.32.3.15.41
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Sheng-I Chen & Wei-Fu Chen, 2021. "The Optimal Harvest Decisions for Natural and Artificial Maturation Mangoes under Uncertain Demand, Yields and Prices," Sustainability, MDPI, vol. 13(17), pages 1-17, August.
    2. Kusumastuti, Ratih Dyah & Donk, Dirk Pieter van & Teunter, Ruud, 2016. "Crop-related harvesting and processing planning: a review," International Journal of Production Economics, Elsevier, vol. 174(C), pages 76-92.
    3. A J Higgins & L A Laredo, 2006. "Improving harvesting and transport planning within a sugar value chain," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(4), pages 367-376, April.
    4. de Moraes Dutenkefer, Raphael & de Oliveira Ribeiro, Celma & Morgado Mutran, Victoria & Eduardo Rego, Erik, 2018. "The insertion of biogas in the sugarcane mill product portfolio: A study using the robust optimization approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 729-740.
    5. Junqueira, Rogerio de Ávila Ribeiro & Morabito, Reinaldo, 2019. "Modeling and solving a sugarcane harvest front scheduling problem," International Journal of Production Economics, Elsevier, vol. 213(C), pages 150-160.
    6. Grunow, M. & Gunther, H.-O. & Westinner, R., 2007. "Supply optimization for the production of raw sugar," International Journal of Production Economics, Elsevier, vol. 110(1-2), pages 224-239, October.
    7. Bocca, Felipe Ferreira & Rodrigues, Luiz Henrique Antunes & Arraes, Nilson Antonio Modesto, 2015. "When do I want to know and why? Different demands on sugarcane yield predictions," Agricultural Systems, Elsevier, vol. 135(C), pages 48-56.
    8. J V Caixeta-Filho, 2006. "Orange harvesting scheduling management: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(6), pages 637-642, June.
    9. Esteban López-Milán & Lluis Plà-Aragonés, 2014. "A decision support system to manage the supply chain of sugar cane," Annals of Operations Research, Springer, vol. 219(1), pages 285-297, August.
    10. Reis, Silvia Araújo & Leal, José Eugenio, 2015. "A deterministic mathematical model to support temporal and spatial decisions of the soybean supply chain," Journal of Transport Geography, Elsevier, vol. 43(C), pages 48-58.
    11. Ahumada, Omar & Villalobos, J. Rene, 2009. "Application of planning models in the agri-food supply chain: A review," European Journal of Operational Research, Elsevier, vol. 196(1), pages 1-20, July.

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