具有置信函数的鲁棒优化
Robust optimization with belief functions
摘要：
本文考虑了一个目标函数系数不确定的优化问题。不确定性是通过提供一个离散的场景集，包含目标函数系数的可能实现指定。在传统的和可能性设置的信念函数的概念被用来定义一组可接受的概率分布的场景集。广义Hurwicz准则，然后用于计算的解决方案。在本文中，由此产生的问题的复杂性进行了探讨。提出了一些精确和近似的求解方法。
英文摘要：
In this paper, an optimization problem with uncertain objective function coefficients is considered. The uncertainty is specified by providing a discrete scenario set, containing possible realizations of the objective function coefficients. The concept of belief function in the traditional and possibilistic setting is applied to define a set of admissible probability distributions over the scenario set. The generalized Hurwicz criterion is then used to compute a solution. In this paper, the complexity of the resulting problem is explored. Some exact and approximation methods of solving it are proposed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
