Scenario driven optimal sequencing under deep uncertainty. (June 2015)

• Record Type: Journal Article
• Authors: Beh, Eva H.Y.; Maier, Holger R.; Dandy, Graeme C.
• Is Part Of: Environmental modelling & software. Volume 68(2015:Jun.)
• Page Start: 181
• Page End: 195
• Publication Date: 2015-06
• Subjects: Optimal sequencing/scheduling -- Multi-objective optimisation -- Deep uncertainty -- Robustness -- Scenarios -- Urban water supply augmentation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.02.006
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The optimal sequencing/scheduling of activities is vital in many areas of environmental and water resources planning and management. In order to account for deep uncertainty surrounding future conditions, a new optimal scheduling approach is introduced in this paper, which consists of three stages. Firstly, a portfolio of diverse sequences that are optimal under a range of plausible future conditions is generated. Next, global sensitivity analysis is used to assess the robustness of these sequences and to determine the relative contribution of future uncertain variables to this robustness. Finally, an optimal sequence is selected for implementation. The approach is applied to the optimal sequencing of additional potential water supply sources, such as desalinated-, storm- and rain-water, for the southern Adelaide water supply system, over a 40 year planning horizon at 10-year intervals. The results indicate that the proposed approach is useful in identifying optimal sequences under deep uncertainty. Highlights: 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.