Skip to main content
Springer Nature Link
Log in

An optimization model for combined selecting, planting and harvesting sugarcane varieties

  • S.I. : Agriculture Analytics, BigData and Sustainable Development
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

The problem of selecting sugarcane varieties has been widely discussed due to its computational complexity and its great impact for the sugar and ethanol industry. This paper proposes a new integrated mathematical programming model to deal with the selection of sugarcane varieties to be planted and the determination of the optimal period for planting and harvesting in order to increase production in the sugarcane industry. The proposed model optimizes the production of sugarcane and improves the quality of biomass whilst satisfying the main constraints imposed by sugarcane companies. The problem is modelled as an integer linear program and solved using an exact method to generate optimal solutions for small and medium problems. For large problems, metaheuristic approaches based on Genetic Algorithm and Variable Neighbourhood Search are proposed. According to the results, the proposed methodology provides sugarcane company managers with decision support in selecting the most suitable varieties and in determining the best period to plant and harvest their sugarcane.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Bolboaca, S., Jantschi, L., Balan, V., Diudea, M., & Sestras, R. (2010). State of art in genetic algorithms for agricultural systems. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 38(3), 51–63.

    Google Scholar 

  • Brimberg, J., & Mladenovic, N. (1996). A variable neighbourhood algorithm for solving the continuous location–allocation problem. Studies of Locational Analysis, 10, 1–12.

    Google Scholar 

  • Calija, A. V., Higgins, J. L. P. A., & Bielig, C. D. (2001). An operations research approach to the problem of the sugarcane selection. Annals of Operations Research, 108, 123–142.

    Article  Google Scholar 

  • Colin, E. C. (2009). Mathematical programming accelerates implementation of agro-industrial sugarcane complex. European Journal of Operational Research, 199(1), 232–235.

    Article  Google Scholar 

  • Engelke, J. (2002). Sugarcane: Measuring commercial quality. Tech. rep., FARMNOTE Department of Agriculture, Kununurra, Australia, n. 23.

  • Florentino, H. O., Irawan, C., Aliano, A. F., Jones, D. F., Cantane, D. R., & Nervis, J. J. (2017). A multiple objective methodology for sugarcane harvest management with varying maturation periods. Annals of Operations Research,. https://doi.org/10.1007/s10479-017-2568-2.

    Article  Google Scholar 

  • Florentino, H. O., & Pato, M. V. (2014). A bi-objective genetic approach for the selection of sugarcane varieties to comply with environmental and economic requirements. Journal of the Operational Research Society, 65(6), 842–854.

    Article  Google Scholar 

  • Florentino, H. O., Moreno, E. V., & Sartori, M. M. A. P. (2008). Multiobjective optimization of economic balances of sugarcane harvest biomass. Scientia Agricola, 65, 561–564.

    Article  Google Scholar 

  • Florentino, H. O., Lima, A. D. D., Carvalho, L. R. D., Balbo, A. R., & Homem, T. P. D. (2011). Multiobjective 0–1 integer programming for the use of sugarcane residual biomass in energy cogeneration. International Transactions in Operational Research, 18(5), 605–615.

    Article  Google Scholar 

  • Goldberg, D. E. (1989). Genetic Algorithms in Search. Optimization and Machine Learning: Addison-Wesley, Reading, Massachusetts. https://doi.org/10.1023/A:1022602019183.

  • Hansen, P., & Mladenovic, N. (1997). Variable neighbourhood search for the p-median. Location Science, 5, 207–225.

    Article  Google Scholar 

  • Higgins, A., & Laredo, L. A. (2006). Improving harvesting and transport planning within a sugar value chain. Journal of the Operational Research Society, 57, 367–376.

    Article  Google Scholar 

  • Higgins, A., Muchow, R., & Rudd, A. (1998). Optimising harvest date in sugar production: A case study for the mossman mill region in australia: I. Development of operations research model and solution. Field Crops Research, 57(2), 153–162.

    Article  Google Scholar 

  • Higgins, A., Antony, G., Sandell, G., Davies, I., Prestwidge, D., & Andrew, B. (2004). A framework for integrating a complex harvesting and transport system for sugar production. Agricultural Systems, 82(2), 99–115.

    Article  Google Scholar 

  • Holland, J. H. (1992). Adaptation in natural and artificial systems. Cambridge: MIT Press. https://doi.org/10.1137/1018105.

    Book  Google Scholar 

  • Jena, S. D., & Poggi, M. (2013). Harvest planning in the brazilian sugar cane industry via mixed integer programming. European Journal of Operational Research, 230(2), 374–384.

    Article  Google Scholar 

  • Leboreiro, J., & Hilaly, A. K. (2011). Biomass transportation model and optimum plant size for the production of ethanol. Bioresource Technology, 102(3), 2712–2723.

    Article  Google Scholar 

  • Paiva, R. R. P. O., & Morabito, R. (2009). An optimization model for the aggregate production planning of a Brazilian sugar and ethanol milling company. Annals of Operations Research, 169, 117.

    Article  Google Scholar 

  • Piewthongngam, K., Pathumnakul, S., & Setthanan, K. (2009). Application of crop growth simulation and mathematical modeling to supply chain management in the thai sugar industry. Agricultural Systems, 102(1), 58–66.

    Article  Google Scholar 

  • Ramos, R. P., Isler, P. R., Florentino, H. O., Jones, D., & Nervis, J. J. (2016). An optimization model for the combined planning and harvesting of sugarcane with maturity considerations. African Journal of Agricultural Research, 11, 3950–3958.

    Article  Google Scholar 

  • Sartori, M. M. P., de Oliveira, F. H., Basta, C., & Leão, A. L. (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, 26(11), 1031–1040.

    Article  Google Scholar 

  • Scarpari, M., & Beauclair, E. (2010). Optimized agricultural planning of sugarcane using linear programming. Investigacion operacional, 31, 126–132.

    Google Scholar 

  • Shukla, M., & Jharkharia, S. (2014). Harvest scheduling to reduce waste in agri-fresh produce supply chains: An artificial immune system-based solution approach. International Journal of Planning and Scheduling, 2(1), 14–39.

    Article  Google Scholar 

Download references

Acknowledgements

To Brazilian foundations: Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq (Grant Number 312551/2019-3), Pró-Reitoria de Pesquisa da UNESP - PROPE, Fundação para o Desenvolvimento da UNESP - FUNDUNESP and Fundação de Amparo ã Pesquisa do Estado de São Paulo - FAPESP (Grant Numbers 2014/01604-0 and 2014/04353-8) for their financial support.

Author information

Authors and Affiliations

  1. Departamento de Bioestatística IB UNESP, Botucatu, SP, Brasil

    Helenice de O. Florentino & Daniela R. Cantane

  2. School of Mathematics and Physics, Centre for Operational Research and Logistics, University of Portsmouth, Lion Gate Building, Lion Terrace, Portsmouth, PO1 3HF, UK

    Dylan F. Jones, Djamila Ouelhadj & Banafesh Khosravi

  3. Nottingham University Business School China, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo, 315100, China

    Chandra Ade Irawan

Authors
  1. Helenice de O. Florentino
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Dylan F. Jones
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Chandra Ade Irawan
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Djamila Ouelhadj
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Banafesh Khosravi
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Daniela R. Cantane
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Helenice de O. Florentino.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Florentino, H.d.O., Jones, D.F., Irawan, C.A. et al. An optimization model for combined selecting, planting and harvesting sugarcane varieties. Ann Oper Res 314, 451–469 (2022). https://doi.org/10.1007/s10479-020-03610-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-020-03610-y

Keywords