Strategic and Tactical Modeling in the Argentine Sugar Industry

  • Gustavo BraierEmail author
  • Javier Marenco
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 181)


This chapter describes the application of linear programming techniques to the automatic planning of manufacturing and distribution decisions at the main sugar producer in Argentina. We summarize the production and logistics chain for the sugar business of this company, and present a linear programming model representing the key planning decisions within these processes. We provide details on the implementation of a software tool for managing the data, solving the model, and analyzing the results. That software tool and embedded model allowed the sugar planning team to improve the planning decisions by having a global comprehension of a very complex decision structure and analyzing multiple scenarios. At the same time, the team obtained a better understanding of the limits and potential of the available industrial and logistical facilities.


Sugar Cane Linear Programming Model Industrial Complex Stock Level Mixed Integer Programming Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Fabio Bardín, Miguel Casares, Hugo Díaz, Miguel Kremer, Alejandro Peuchot, Rodolfo Roballos, Alberto Salvado, and Alejandro Serrano from Ledesma SAAI. The authors are indebted to Prof. James Bookbinder for his valuable comments and remarks, which greatly improved this chapter.


  1. Chiadamrong N, Kawtummachai R (2008) A methodology to support decision-making on sugar distribution for export channel: a case study of Thai sugar industry. Comput Electron Agric 64(2):248–261CrossRefGoogle Scholar
  2. Díaz J, Pérez I (2000) Simulation and optimization of sugar cane transportation in harvest season. In: Proceedings of the 32nd winter simulation conference, pp 1114–1117Google Scholar
  3. Higgins A (1999) Optimizing cane supply decisions within a sugar mill region. J Sched 2(5):229–244CrossRefGoogle Scholar
  4. Higgins A (2002) Australian sugar mills optimize harvester rosters to improve production. Interfaces 32(3):15–25CrossRefGoogle Scholar
  5. Higgins A, Beashel G, Harrison A (2006) Scheduling of brand production and shipping within a sugar supply chain. J Oper Res Soc 57:490–498CrossRefGoogle Scholar
  6. Hollingworth J, Allsop J, Butterfield D, Swart R, Smith M, Woodbury W, Turnbull K, Bonavita J, Chandler D, Banks D, Almannai K, Cashman M, Blanton II P, Wu S, Winters C (2000) C++ builder 5 developer’s guide. Sams Professional, New YorkGoogle Scholar
  7. Kostina A, Guillén-Gosálbeza G, Meleb F, Bagajewiczc M, Jiméneza L (2010) Integrating pricing policies in the strategic planning of supply chains: a case study of the sugar cane industry in Argentina. Comput Aided Chem Eng 28:103–108CrossRefGoogle Scholar
  8. Lejars C, Le Gal P, Auzoux S (2008) A decision support approach for cane supply management within a sugar mill area. Comput Electron Agric 60:239–249CrossRefGoogle Scholar
  9. Le Gal P, Le Masson J, Bezuidenhout C, Lagrange L (2009) Coupled modelling of sugarcane supply planning and logistics as a management tool. Comput Electron Agric 68(2):168–177CrossRefGoogle Scholar
  10. Mosek ApS (2010) The MOSEK optimization tools manual. Mosek ApS, CopenhagenGoogle Scholar
  11. Taechasook P, Sethanan K, Bureerat S (2008) Application of genetic algorithms for sugar cane harvesting decision. Proceedings of the technology and innovation for sustainable development conference, pp 172–177Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Braier & Asociados ConsultoresOlivosArgentina

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