Comparison of robust optimization and info-gap methods for water resource management under deep uncertainty

Comparison of robust optimization and info-gap methods for water resource management under deep uncertainty.
Roach, T.and Kapelan, Z. and Ledbetter, R. and Ledbetter, M.
Journal of Water Resources Planning and Management, 42 (9). ISSN 0733-9496 (2016)

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Abstract:This paper evaluates two established decision-making methods and analyzes their performance and suitability within a water resources management (WRM) problem. The methods under assessment are info-gap (IG) decision theory and robust optimization (RO). The methods have been selected primarily to investigate a contrasting local versus global method of assessing water system robustness to deep uncertainty, but also to compare a robustness model approach (IG) with a robustness algorithm approach (RO), whereby the former selects and analyzes a set of prespecified strategies and the latter uses optimization algorithms to automatically generate and evaluate solutions. The study presents a novel area-based method for IG robustness modeling and assesses the applicability of utilizing the future flows climate change projections in scenario generation for water resource adaptation planning. The methods were applied to a case study resembling the Sussex North Water Resource Zone in England, assessing their applicability at improving a risk-based WRM problem and highlighting the strengths and weaknesses of each method at selecting suitable adaptation strategies under climate change and future demand uncertainties. Pareto sets of robustness to cost are produced for both methods and highlight RO as producing the lower cost strategies for the full range of varying target robustness levels. IG produced the more expensive Pareto strategies due to its more selective and stringent robustness analysis, resulting from the more complex scenario ordering process.
Item Type:Article
Subjects:Water > General
ID Code:1372
Deposited On:20 Jan 2017 12:59
Last Modified:20 Jan 2017 12:59

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