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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.

Item Type:	Article
Authors/Creators:	Randall, M and Montgomery, EJ and Lewis, A
Keywords:	robust optimisation, climate change, crop planning, deep uncertainty
Journal or Publication Title:	Operations Research Perspectives
Publisher:	Elsevier Ltd
ISSN:	2214-7160
DOI / ID Number:	https://doi.org/10.1016/j.orp.2021.100219
Copyright Information:	© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Related URLs:	Google Scholar: Montgomery, EJ UTAS Profile: Montgomery, EJ
Item Statistics:	View statistics for this item

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