Interactive Multiobjective Optimization Under Uncertainty
提出一种通用多目标算法来处理概率结果的不确定性，适用于离散自然状态的多准则框架，并通过模拟和行为实验验证了该方法在高维问题中的可行性。
Uncertainty presents unique difficulties in multiobjective optimization problems, because decision makers are faced with risky situations requiring analysis of multiple outcomes in differing states of nature. Very few direct choice (interactive) multiobjective methods are capable of addressing problems with probabilistic outcomes. We thus propose a general multiobjective algorithm which accommodates uncertainty. The method is appropriate for use in a multiple criteria framework with a discrete number of states of nature. Without loss of generality, and in the interest of simplicity of exposition, our method is explored and developed in the context of a bicriterion optimization problem using a two stage mathematical programming model. Simulation and behavioral experiments are conducted which verify that the method is viable for problems with greater dimensionality.