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A note on robustness of the min-max solution to multi-objective linear programs

机译:关于多目标线性规划的最小-最大解的鲁棒性的说明

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The challenge of using scalarising methods in multi-objective optimisation results from the choice of the method, which may not be apparent, and given that a method has been selected, from the choice of the values of the scalarising parameters. In general, these values may be unknown and the decision maker faces a difficult situation of making a choice possibly under a great deal of uncertainty. Due to its effectiveness, the robust optimisation approach of Ben-Tal and Nemirovski is applied to resolve the uncertainty carried in scalarised multi-objective linear programs (MOLPs). A robust counterpart is examined for six different scalarisations of the MOLP yielding robust (weakly) efficient solutions to the original MOLP. The study reveals that the min-max optimal solution emerges as a robust (weakly) efficient solution for five out of the six scalarisations. The implications of this result are also discussed.
机译:在多目标优化中使用标量方法的挑战来自方法的选择(可能不明显),并且鉴于已选择了一种方法,因此要从标量参数的值中进行选择。通常,这些值可能是未知的,决策者可能会面临很大不确定性的艰难抉择。由于其有效性,Ben-Tal和Nemirovski的鲁棒优化方法被用于解决标量多目标线性程序(MOLP)中所携带的不确定性。检查了鲁棒的对应项,以获取MOLP的六个不同的标量,从而得出了原始MOLP的鲁棒(弱)有效解决方案。研究表明,对于六个标量中的五个,最小-最大最佳解决方案作为一种鲁棒的(弱)有效解决方案出现。还讨论了此结果的含义。

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