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An optimization model for the aggregate production planning of a Brazilian sugar and ethanol milling company

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  • Rafael Paiva
  • Reinaldo Morabito

Abstract

This work presents an optimization model to support decisions in the aggregate production planning of sugar and ethanol milling companies. The mixed integer programming formulation proposed is based on industrial process selection and production lot-sizing models. The aim is to help the decision makers in selecting the industrial processes used to produce sugar, ethanol and molasses, as well as in determining the quantities of sugarcane crushed, the selection of sugarcane suppliers and sugarcane transport suppliers, and the final product inventory strategy. The planning horizon is the whole sugarcane harvesting season and decisions are taken on a discrete fraction of time. A case study was developed in a Brazilian mill and the results highlight the applicability of the proposed approach. Copyright Springer Science+Business Media, LLC 2009

Suggested Citation

  • Rafael Paiva & Reinaldo Morabito, 2009. "An optimization model for the aggregate production planning of a Brazilian sugar and ethanol milling company," Annals of Operations Research, Springer, vol. 169(1), pages 117-130, July.
    • Handle: RePEc:spr:annopr:v:169:y:2009:i:1:p:117-130:10.1007/s10479-008-0428-9
    DOI: 10.1007/s10479-008-0428-9
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    References listed on IDEAS

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    1. Hugo T. Y. Yoshizaki & Antonio R. N. Muscat & Jorge L. Biazzi, 1996. "Decentralizing Ethanol Distribution in Southeastern Brazil," Interfaces, INFORMS, vol. 26(6), pages 24-34, December.
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    5. Sartori, Maria Márcia Pereira & Florentino, Helenice de Oliveira & Basta, Cesar & Leão, Alcides Lopes, 2001. "Determination of the optimal quantity of crop residues for energy in sugarcane crop management using linear programming in variety selection and planting strategy," Energy, Elsevier, vol. 26(11), pages 1031-1040.
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    Cited by:

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    2. da Silva, Aneirson Francisco & Marins, Fernando Augusto Silva, 2014. "A Fuzzy Goal Programming model for solving aggregate production-planning problems under uncertainty: A case study in a Brazilian sugar mill," Energy Economics, Elsevier, vol. 45(C), pages 196-204.
    3. Kamal Lamsal & Philip C. Jones & Barrett W. Thomas, 2017. "Sugarcane Harvest Logistics in Brazil," Transportation Science, INFORMS, vol. 51(2), pages 771-789, May.
    4. Helenice de O. Florentino & Dylan F. Jones & Chandra Ade Irawan & Djamila Ouelhadj & Banafesh Khosravi & Daniela R. Cantane, 2022. "An optimization model for combined selecting, planting and harvesting sugarcane varieties," Annals of Operations Research, Springer, vol. 314(2), pages 451-469, July.
    5. 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.
    6. Waldemarsson, Martin & Lidestam, Helene & Karlsson, Magnus, 2017. "How energy price changes can affect production- and supply chain planning – A case study at a pulp company," Applied Energy, Elsevier, vol. 203(C), pages 333-347.
    7. Camila de Lima & Antonio Roberto Balbo & Thiago Pedro Donadon Homem & Helenice de Oliveira Florentino Silva, 2017. "A hybrid approach combining interior-point and branch-and-bound methods applied to the problem of sugar cane waste," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(2), pages 147-164, February.

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