Optimization of the Supply Chain Management of Sugarcane in Cuba

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 224)


In this chapter the authors present and discuss the problem of planning sugarcane harvesting–transportation–delivering to the mill for the supply chain management of the sugarcane. Furthermore, an optimization model for practical use is formulated and embedded into a decision support system (DSS) for planning daily operations. The objective function seeks to minimize transportation costs while assuring cane supply to the sugar mill. The model determines the fields to harvest, the cutting–loading–transport means for such operation, and the roster for each employee. Although the model has been developed and tested under Cuban conditions, it can easily be adapted to different situations updating the parameters of the model and the database of the DSS. Main reported savings represent 8 % of the fuel cost, apart from the workload reduction of mill managers in planning tasks.


Sugarcane supply chain MILP Decision support system Resource allocation 


  1. Bezuidenhout CN, Singels A (2007a) Operational forecasting of South African sugarcane production: Part 1—System description. Agric Syst 92:23–38CrossRefGoogle Scholar
  2. Bezuidenhout CN, Singels A (2007b) Operational forecasting of South African sugarcane production: Part 2—System evaluation. Agric Syst 92:39–51CrossRefGoogle Scholar
  3. Díaz JA, Pérez IG (2000) Simulation and optimization of sugar cane transportation in harvest season. In: The proceedings of winter simulation conference, Miami, December 2000, pp 1114–1117Google Scholar
  4. Grunow M, Günther H-O, Westinner R (2007) Supply optimization for the production of raw sugar. Int J Prod Econ 110:224–239CrossRefGoogle Scholar
  5. Higgins AJ (1999) Optimizing cane supply decisions within a sugar mill region. J Scheduling 2:229–244CrossRefGoogle Scholar
  6. Higgins AJ (2002) Australian sugar mills optimize harvester rosters to improve production. Interfaces 32(3):15–25CrossRefGoogle Scholar
  7. Higgins A (2006) Scheduling of road vehicles in sugarcane transport: a case study at an Australian sugar mill. Eur J Oper Res 170:987–1000CrossRefGoogle Scholar
  8. Higgins AJ, Laredo LA (2006) Improving harvesting and transport planning within a sugar value chain. J Oper Res Soc 57:367–376CrossRefGoogle Scholar
  9. Higgins AJ, Muchow RC (2003) Assessing the potential benefits of alternative cane supply arrangements in the Australian sugar industry. Agric Syst 76:623–638CrossRefGoogle Scholar
  10. Higgins AJ, Thorburn P, Archer A, Jakku E (2007) Opportunities for value chain research in sugar industries. Agric Syst 94:611–621CrossRefGoogle Scholar
  11. Jena SD, Poggi M (2013) Harvest planning in the Brazilian sugar cane industry via mixed integer programming. Eur J Oper Res 230:374–384CrossRefGoogle Scholar
  12. Le Gal P-Y, Lyne PWL, Meyer E, Soler L-G (2008) Impact of sugarcane supply scheduling on mill sugar production: a South African case study. Agric Syst 96:64–74CrossRefGoogle Scholar
  13. Le Gal P-Y, Le Masson J, Bezuidenhout CN, Lagrange LF (2009) Coupled modelling of sugarcane supply planning and logistics as a management tool. Comput Electron Agr 68:168–177CrossRefGoogle Scholar
  14. Lejars C, Le Gal P-Y, Auzoux S (2008) A decision support approach for cane supply management within a sugar mill area. Comput Electron Agr 60:239–249CrossRefGoogle Scholar
  15. López E, Miquel S, Plà LM (2004) El problema del transporte de la caña de azúcar en Cuba. Rev Invest Oper 25:148–157Google Scholar
  16. López E, Miquel S, Plà LM (2006) Sugar cane transportation in Cuba, a case study. Eur J Oper Res 174:374–386CrossRefGoogle Scholar
  17. Martin F, Pinkney A, Xinghuo XY (2001) Cane railway scheduling via constraint logic programming: labelling order and constraints in a real-live application. Ann Oper Res 108:193–209CrossRefGoogle Scholar
  18. Mula J, Peidro D, Díaz-Madroñero M, Vicens E (2010) Mathematical programming models for supply chain production and transport planning. Eur J Oper Res 204:377–390CrossRefGoogle Scholar
  19. Pavia R, Morabito R (2009) An optimisation model for the aggregate production planning of a Brazilian sugar and ethanol milling company. Ann Oper Res 161:117–130CrossRefGoogle Scholar
  20. Piewthongngam K, Pathumnakul S, Setthanan K (2009) Application of crop growth simulation and mathematical modeling to supply chain management in the Thai sugar industry. Agric Syst 102:58–66CrossRefGoogle Scholar
  21. Plà LM, Sandars D, Higgins A (2014) A perspective on operational research prospects for agriculture. J Oper Res Soc 65:1078–1089. doi: 10.1057/jors.2013.45 Google Scholar
  22. Rizzoli AE, Fornara N, Gambardella LM (2002) A simulation tool for combined rail/road transport in intermodal terminals. Math Comput Simulat 59:57–71CrossRefGoogle Scholar
  23. Scarpari MS, de Beauclair EGF (2010) Optimized agricultural planning of sugarcane using linear programming. Rev Invest Oper 31:126–132Google Scholar
  24. Scarpari MS, Plà LM, de Beauclair EGF (2008) La optimización del cultivo de variedades de caña de azúcar. Rev Invest Oper 29:26–34Google Scholar
  25. Schrage L (1997) Optimization modeling with LINDO, 5th edn. Duxbury Press, ITP, New YorkGoogle Scholar
  26. Semenzato R (1995) A simulation study of sugar cane harvesting. Agric Syst 47:427–437CrossRefGoogle Scholar
  27. Stray BJ, van Vuuren JH, Bezuidenhout CN (2012) An optimisation-based seasonal sugarcane harvest scheduling decision support system for commercial growers in South Africa. Comput Electron Agr 83:21–31CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media New York 2015

Authors and Affiliations

  • Esteban López-Milán
    • 1
  • Lluis M. Plà-Aragonés
    • 2
  1. 1.Mechanical Engineering DepartmentUniversity of HolguinHolguínCuba
  2. 2.Department of MathematicsUniversity of LleidaLleidaSpain

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