Annals of Operations Research

, Volume 267, Issue 1–2, pp 153–177 | Cite as

A multiple objective methodology for sugarcane harvest management with varying maturation periods

  • Helenice de Oliveira Florentino
  • Chandra Irawan
  • Angelo Filho Aliano
  • Dylan F. Jones
  • Daniela Renata Cantane
  • Jonis Jecks Nervis
Multiple Objective Optimization


This paper addresses the management of a sugarcane harvest over a multi-year planning period. A methodology to assist the harvest planning of the sugarcane is proposed in order to improve the production of POL (a measure of the amount of sucrose contained in a sugar solution) and the quality of the raw material, considering the constraints imposed by the mill such as the demand per period. An extended goal programming model is proposed for optimizing the harvest plan of the sugarcane so the harvesting point is as close as possible to the ideal, considering the constrained nature of the problem. A genetic algorithm (GA) is developed to tackle the problem in order to solve realistically large problems within an appropriate computational time. A comparative analysis between the GA and an exact method for small instances is also given in order to validate the performance of the developed model and methods. Computational results for medium and large farm instances using GA are also presented in order to demonstrate the capability of the developed method. The computational results illustrate the trade-off between satisfying the conflicting goals of harvesting as closely as possible to the ideal and making optimum use of harvesting equipment with a minimum of movement between farms. They also demonstrate that, whilst harvesting plans for small scale farms can be generated by the exact method, a meta-heuristic GA method is currently required in order to devise plans for medium and large farms.


Multiple objective optimization Goal programming Genetic algorithm Sugarcane harvest planning 



The authors wish to thank the Brazilian foundations FAPESP (Grant Nos. 2014/01604-0 and 2014/04353-8), CNPq (Grant No. 303267/2011-9), PROEPE (UNESP) and FUNDUNESP. Also, to the Institute of Mathematics, Statistics and Scientific Computation belonging to UNICAMP and FAPESP (Grant 2013/06035-0), for their financial support. The authors also wish to thank the two anonymous referees whose comments helped shape the final version of this paper.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Helenice de Oliveira Florentino
    • 1
  • Chandra Irawan
    • 2
  • Angelo Filho Aliano
    • 3
  • Dylan F. Jones
    • 2
  • Daniela Renata Cantane
    • 1
  • Jonis Jecks Nervis
    • 4
  1. 1.Department of BiostatisticsUNESP - Univ Estadual PaulistaBotucatuBrazil
  2. 2.Department of Mathematics, Centre for Operational Research and LogisticsUniversity of PortsmouthPortsmouthUK
  3. 3.Academic Department of MathematicsFederal Technology University of ParanáApucaranaBrazil
  4. 4.Energy in Agriculture, FCAUNESP - Univ Estadual PaulistaBotucatuBrazil

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