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首页> 外文会议>International Groundwater Symposium 2002; Mar 25-28, 2002; Berkeley, California >MANAGING UNCERTAINTY IN RISK-BASED REMEDIATION DESIGN: AN OPTIMIZATION FRAMEWORK USING A NOISY GENETIC ALGORITHM
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MANAGING UNCERTAINTY IN RISK-BASED REMEDIATION DESIGN: AN OPTIMIZATION FRAMEWORK USING A NOISY GENETIC ALGORITHM

机译：基于风险的补救设计中的不确定性管理：基于噪声遗传算法的优化框架

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摘要

A risk-based remediation design model was presented that combines a noisy genetic algorithm with risk assessnient models to simultaneously assess risk and identify cost-effective and reliable designs. The noisy genetic algorithm evaluates candidate designs using sampling of the fitness function, which is the sum of the objective function and constraints. A three-step methodology for determining the optimal sample size was also summarized. Application of the model to a case study demonstrated that highly reliable designs can be identified with minimal sampling, revealing the efficiency of the noisy GA approach. Several designs were identified with a range of reliabilities, providing useful information in assessing the tradeoffs between cost and reliability. Research is ongoing to complete development of the risk management model, which will ultimately allow tradeoffs among cost, risk, cleanup time, and uncertainty to be considered in a multi-objective format during the remediation design process. With such information available, remediation negotiations will be able to focus on the design issues that have the most effect on the cost and reliability of the remediation.
机译：提出了基于风险的补救设计模型，该模型将嘈杂的遗传算法与风险评估模型结合在一起，以同时评估风险并确定具有成本效益的可靠设计。噪声遗传算法使用适合度函数的采样来评估候选设计，适合度函数是目标函数和约束的总和。还总结了确定最佳样本量的三步法。该模型在案例研究中的应用表明，可以通过最少的采样来确定高度可靠的设计，从而揭示出噪声遗传算法的效率。确定了具有可靠性范围的几种设计，为评估成本和可靠性之间的折衷提供了有用的信息。正在进行研究以完成风险管理模型的开发，该模型最终将允许在补救设计过程中以多​​目标格式考虑成本，风险，清理时间和不确定性之间的折衷。利用此类信息，补救协商将能够集中于对补救的成本和可靠性影响最大的设计问题。

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