A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding
A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding
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DOI：
10.1016/j.ejor.2016.11.038
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
Mohammad Ghaderi;F. J. Ruiz;N. Agell
中科院分区：
文献类型：
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作者：
Mohammad Ghaderi;F. J. Ruiz;N. Agell
提出了一种新的多准则决策辅助偏好分解框架。该方法的目的是从一组间接的两两比较中推断出非单调的加性偏好模型。偏好模型被表示为一组边缘值函数，并针对其复杂性最大化推断出的偏好模型的区分能力。为了推断与所提供的偏好信息兼容的值函数，所提出的方法导致易于解决的线性规划优化问题。新方法的适用性和有效性证明了一个彻底的实验分析，涵盖了广泛的决策问题。
A new framework for preference disaggregation in multiple criteria decision aiding is introduced. The proposed approach aims to infer non-monotonic additive preference models from a set of indirect pairwise comparisons. The preference model is presented as a set of marginal value functions and the discriminatory power of the inferred preference model is maximized against its complexity. To infer a value function that is compatible with the supplied preference information, the proposed methodology leads to a linear programming optimization problem that is easy to solve. The applicability and effectiveness of the new methodology is demonstrated in a thorough experimental analysis covering a broad range of decision problems.