Abstract
A robust risk analysis method (RRAM) is developed for water resource decision making under uncertainty. This method incorporates interval-parameter programming and robust optimization within a stochastic programming framework. In the RRAM formulation, penalties are exercised with the recourse against any infeasibility, and robustness measures are introduced to examine the variability of the second stage costs which are above the expected levels. In this study, a number of weighting levels are considered which correspond to the robustness levels of risk control. Generally, a plan with a higher robust level would better resist from system risk. Thus, decision with a lower robust level can correspond to a higher risk of system failure. There is a tradeoff between system cost and system reliability. The RRAM is applied to a case of water resource management. The modeling results can help generate desired decision alternatives that will be particularly useful for risk-aversive decision makers in handling high-variability conditions. The results provide opportunities to managers to make decisions based on their own preferences on system stability and economy, and ensure that the management policies and plans be made with reasonable consideration of both system cost and risk.
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Acknowledgments
This Research was supported by the Natural Sciences Foundation of China (51190095), the Open Research Fund Program of State Key Laboratory of Hydro-science and Engineering (sklhse-2012-A-03), the Program for Changjiang Scholars and Innovative Research Team in University (IRT1127), and the Program for New Century Excellent Talents in University (NCET-10-0376). The authors are grateful to the editors and the anonymous reviewers for their insightful comments and suggestions.
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Chen, C., Huang, G.H., Li, Y.P. et al. A robust risk analysis method for water resources allocation under uncertainty. Stoch Environ Res Risk Assess 27, 713–723 (2013). https://doi.org/10.1007/s00477-012-0634-5
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DOI: https://doi.org/10.1007/s00477-012-0634-5