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Biobjective robust optimization over the efficient set for Pareto set reduction

机译:针对帕累托集约简的有效集的双目标鲁棒优化

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This paper presents a biobjective robust optimization formulation for identifying robust solutions from a given Pareto set. The objectives consider both solution and model robustness when the exact values of the selected solution are affected by uncertainty. The problem is formulated equivalently as a model with uncertainty on the constraint parameters and objective function coefficients. Structural properties and a solution algorithm are developed for the case of multiobjective linear programs. The algorithm is based on facial decomposition; each subproblem is a biobjective linear program and is related to an efficient face of the multiobjective program. The resulting Pareto set reduction methodology allows the handling of continuous and discrete Pareto sets, and can be generalized to consider criteria other than robustness. The use of secondary criteria to further break ties among the many efficient solutions provides opportunities for additional trade-off analysis in the space of the secondary criteria. Examples illustrate the algorithm and characteristics of solutions obtained. (C) 2016 Elsevier B.V. All rights reserved.
机译:本文提出了一种用于确定给定Pareto集鲁棒解决方案的双目标鲁棒优化公式。当所选解决方案的精确值受不确定性影响时,目标会同时考虑解决方案和模型的稳健性。将该问题等效地表示为对约束参数和目标函数系数具有不确定性的模型。针对多目标线性程序,开发了结构特性和求解算法。该算法基于面部分解;每个子问题都是双目标线性程序,并且与多目标程序的有效面有关。所得的帕累托集减少方法论允许处理连续和离散的帕累托集,并且可以被普遍考虑以考虑除鲁棒性以外的标准。使用次级标准来进一步打破许多有效解决方案之间的联系,为在次级标准空间内进行其他折衷分析提供了机会。实例说明了所获得解决方案的算法和特征。 (C)2016 Elsevier B.V.保留所有权利。

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