TY - JOUR
T1 - Robust temporal optimisation for a crop planning problem under climate change uncertainty
AU - Randall, Marcus
AU - Montgomery, James
AU - Lewis, Andrew
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85121979745&partnerID=8YFLogxK
U2 - 10.1016/j.orp.2021.100219
DO - 10.1016/j.orp.2021.100219
M3 - Article
SN - 2214-7160
VL - 9
JO - Operations Research Perspectives
JF - Operations Research Perspectives
M1 - 100219
ER -
