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Robust optimisation approach applied to the analysis of production / logistics and crop planning in the tomato processing industry

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  • Cleber D. Rocco
  • Reinaldo Morabito

Abstract

The soluble solids content in the tomato fruit, also known as ‘brix’, and the crop yield are the most relevant uncertain parameters to determine technical and economic performance in the tomato processing industry. This paper presents a linear programming model and three robust optimisation models to deal with data uncertainty in the analysis of crop, logistics and industrial tactical planning in this industry. We focused the analysis on the production and logistics costs due to the impacts of unfavourable disturbances on the amount of soluble solids and the quantity of tomatoes processed in the system. A typical industry in this sector collaborated with this study by providing real data of its production, logistics and crop plans and with in-depth discussions. From the results, some general conclusions were outlined and we discuss the benefits of adopting the robust optimisation approach instead of a deterministic one. The robust approach proved to be a powerful tool for elaborating scenarios for uncertainty analysis in medium-term decisions, as described in this study, and clearly has potential to be employed in real contexts.

Suggested Citation

  • Cleber D. Rocco & Reinaldo Morabito, 2016. "Robust optimisation approach applied to the analysis of production / logistics and crop planning in the tomato processing industry," International Journal of Production Research, Taylor & Francis Journals, vol. 54(19), pages 5842-5861, October.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:19:p:5842-5861
    DOI: 10.1080/00207543.2016.1181284
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    References listed on IDEAS

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    1. Cleber Damião Rocco & Reinaldo Morabito, 2014. "Scheduling of production and logistics operations of steam production systems in food industries: a case study of the tomato processing industry," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(12), pages 1896-1904, December.
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    Cited by:

    1. Tuğçe Taşkıner & Bilge Bilgen, 2021. "Optimization Models for Harvest and Production Planning in Agri-Food Supply Chain: A Systematic Review," Logistics, MDPI, vol. 5(3), pages 1-27, August.

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