|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4959437||1364862||2018||12 صفحه PDF||ندارد||دانلود رایگان|
â¢We consider robust combinatorial optimization problems with parametrized uncertainty.â¢For minâmax robustness, we develop methods to find a set of robust solutions.â¢This set contains an optimal robust solution for each possible uncertainty size.â¢For minâmax regret robustness we consider the inverse robust problem.â¢The aim is to find the largest uncertainty such that a fixed solution stays optimal.
In robust optimization, the general aim is to find a solution that performs well over a set of possible parameter outcomes, the so-called uncertainty set. In this paper, we assume that the uncertainty size is not fixed, and instead aim at finding a set of robust solutions that covers all possible uncertainty set outcomes. We refer to these problems as robust optimization with variable-sized uncertainty. We discuss how to construct smallest possible sets of minâmax robust solutions and give bounds on their size.A special case of this perspective is to analyze for which uncertainty sets a nominal solution ceases to be a robust solution, which amounts to an inverse robust optimization problem. We consider this problem with a minâmax regret objective and present mixed-integer linear programming formulations that can be applied to construct suitable uncertainty sets.Results on both variable-sized uncertainty and inverse problems are further supported with experimental data.
Journal: European Journal of Operational Research - Volume 264, Issue 1, 1 January 2018, Pages 17-28