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

Cleber D. Rocco and Reinaldo Morabito

International Journal of Production Research, 2016, vol. 54, issue 19, 5842-5861

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.

Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2016.1181284 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:54:y:2016:i:19:p:5842-5861

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2016.1181284

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().