Scenario driven optimal sequencing under deep uncertainty
We develop a multi-objective optimal sequencing approach under deep uncertainty. The approach uses scenarios to develop a portfolio of optimal solutions. Sensitivity analysis is used to assess portfolio performance under deep uncertainty. The approach is illustrated for an urban water supply augmentation case study.
scenarios deep uncertainty urban water supply augmentation optimal sequencing/scheduling multi-objective optimisation robustness