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.
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