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

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 potential error solutions. For multi-year crop planning problem, largest uncertainty is how climate will change over coming decades. The innovation this paper presents are novel methods that allow solver produce feasible under all models tested, simultaneously. Three new measures robustness introduced evaluated. highly robust shown vary little across different projections, maintaining consistent net revenue environmental flow deficits.