Robust temporal optimisation for a crop planning problem under climate change uncertainty

M. Randall, J. Montgomery and A. Lewis

Operations Research Perspectives, 2022, vol. 9, issue C

Abstract: Considering a temporal dimension allows for the delivery of rolling solutions to complex real-world problems. Moving forward in time brings uncertainty, and large margins for potential error in solutions. For the multi-year crop planning problem, the largest uncertainty is how the climate will change over coming decades. The innovation this paper presents are novel methods that allow the solver to produce feasible solutions under all climate models tested, simultaneously. Three new measures of robustness are introduced and evaluated. The highly robust solutions are shown to vary little across different climate change projections, maintaining consistent net revenue and environmental flow deficits.

Keywords: Robust optimisation; Climate change; Crop planning; Deep uncertainty (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2214716021000312
Full text for ScienceDirect subscribers only

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:eee:oprepe:v:9:y:2022:i:c:s2214716021000312

DOI: 10.1016/j.orp.2021.100219

Access Statistics for this article

Operations Research Perspectives is currently edited by Rubén Ruiz Garcia

More articles in Operations Research Perspectives from Elsevier
Bibliographic data for series maintained by Catherine Liu ().