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Orange harvesting scheduling management: a case study

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  • J V Caixeta-Filho

    (University of São Paulo)

Abstract

The competitiveness of Brazil's citrus sector is a function of quality control in the transformation of fruit into juice. The transformation process commences with the harvest, the timing of which significantly affects fruit quality. In this paper, a mathematical model is formulated that links pertinent chemical, biologic, and logistic restrictions to the quality of the fruit to be harvested, applying linear programming theory. The modelling structure was verified and validated with real data from 320 Brazilian farms involved with an annual production of approximately 7 200 000 boxes of oranges. It could be attested that the maximization of the number of boxes of oranges to be harvested (strategy that is still adopted by a representative number of Brazilian citrus farmers, based on the industry advice) does not necessarily correspond to the maximum quantity of total soluble solids (TSS). In many cases, citrus harvested at the optimum TSS point offered higher concentrated juice productivity. The estimated potential benefits ($) from using the proposed model reached figures over 6%.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:6:d:10.1057_palgrave.jors.2602041
    DOI: 10.1057/palgrave.jors.2602041
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    References listed on IDEAS

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    Cited by:

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    4. Yiping Jiang & Liangqi Chen & Yan Fang, 2018. "Integrated Harvest and Distribution Scheduling with Time Windows of Perishable Agri-Products in One-Belt and One-Road Context," Sustainability, MDPI, vol. 10(5), pages 1-13, May.
    5. Wishon, C. & Villalobos, J.R. & Mason, N. & Flores, H. & Lujan, G., 2015. "Use of MIP for planning temporary immigrant farm labor force," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 25-33.
    6. Soto-Silva, Wladimir E. & Nadal-Roig, Esteve & González-Araya, Marcela C. & Pla-Aragones, Lluis M., 2016. "Operational research models applied to the fresh fruit supply chain," European Journal of Operational Research, Elsevier, vol. 251(2), pages 345-355.
    7. 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.
    8. 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.
    9. 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.
    10. Wenbo Zhang & Wilbert Wilhelm, 2011. "OR/MS decision support models for the specialty crops industry: a literature review," Annals of Operations Research, Springer, vol. 190(1), pages 131-148, October.
    11. Víctor M. Albornoz & Lia C. Araneda & Rodrigo Ortega, 2022. "Planning and scheduling of selective harvest with management zones delineation," Annals of Operations Research, Springer, vol. 316(2), pages 873-890, September.
    12. 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.
    13. Ferrer, Juan-Carlos & Mac Cawley, Alejandro & Maturana, Sergio & Toloza, Sergio & Vera, Jorge, 2008. "An optimization approach for scheduling wine grape harvest operations," International Journal of Production Economics, Elsevier, vol. 112(2), pages 985-999, April.
    14. Gómez-Lagos, Javier E. & González-Araya, Marcela C. & Soto-Silva, Wladimir E. & Rivera-Moraga, Masly M., 2021. "Optimizing tactical harvest planning for multiple fruit orchards using a metaheuristic modeling approach," European Journal of Operational Research, Elsevier, vol. 290(1), pages 297-312.

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