Keywords
robust decision making, many-objective optimization, water supply, interactive visual analytics
Start Date
1-7-2012 12:00 AM
Abstract
Portfolios of market-based instruments have been shown to improve the reliability of water supplies, using simulations that utilize a single best estimate of distributions of data to evaluate performance. However, the estimates of problem information and likelihoods could be incorrect, especially when planning for climate change, which can modify streamflow availability, or projecting the trajectories of future water demands. These conditions are termed deep uncertainty, in which decision makers cannot fully conceptualize or agree upon the full range of risks to their system. This presentation will advance a new interactive framework that combines robust decision making (RDM) with many-objective optimization using evolutionary algorithms (MOEA) to confront deep uncertainty for water planning. The framework is demonstrated using a case study that examines a single city's water supply in the Lower Rio Grande Valley (LRGV) in Texas, USA. We use a MOEA to develop a tradeoff set of water supply portfolios for the LRGV, and develop a suite of values for key uncertainties using RDM that represent an ensemble of “states of the world”. Each solution is tested under the ensemble of plausible future states of the world, with interactive visualizations being used to identify robust solutions for the system. Scenario discovery methods that use statistical data mining algorithms are then used to identify what assumptions and system conditions strongly control the cost-effectiveness, efficiency, and reliability of the robust alternatives. The results suggest that combining robust decision making, many-objective optimization, and visual analytics can dramatically improve risk-based planning decisions.
Many-Objective Robust Decision Making for Water Supply Portfolio Planning Under Deep Uncertainty
Portfolios of market-based instruments have been shown to improve the reliability of water supplies, using simulations that utilize a single best estimate of distributions of data to evaluate performance. However, the estimates of problem information and likelihoods could be incorrect, especially when planning for climate change, which can modify streamflow availability, or projecting the trajectories of future water demands. These conditions are termed deep uncertainty, in which decision makers cannot fully conceptualize or agree upon the full range of risks to their system. This presentation will advance a new interactive framework that combines robust decision making (RDM) with many-objective optimization using evolutionary algorithms (MOEA) to confront deep uncertainty for water planning. The framework is demonstrated using a case study that examines a single city's water supply in the Lower Rio Grande Valley (LRGV) in Texas, USA. We use a MOEA to develop a tradeoff set of water supply portfolios for the LRGV, and develop a suite of values for key uncertainties using RDM that represent an ensemble of “states of the world”. Each solution is tested under the ensemble of plausible future states of the world, with interactive visualizations being used to identify robust solutions for the system. Scenario discovery methods that use statistical data mining algorithms are then used to identify what assumptions and system conditions strongly control the cost-effectiveness, efficiency, and reliability of the robust alternatives. The results suggest that combining robust decision making, many-objective optimization, and visual analytics can dramatically improve risk-based planning decisions.
