A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding

被引：49

作者：

Ghaderi, Mohammad ^{[1
]}

Ruiz, Francisco ^{[2
]}

Agell, Nuria ^{[1
]}

机构：

[1] Ramon Llull Univ, ESADE Business Sch, Dept Operat Innovat & Data Sci, Torre Blanca Ave 59, Barcelona 08172, Spain

[2] BarcelonaTech, Dept Automat Control, Vilanova I La Geltru, Spain

来源：

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 2017年 / 259卷 / 03期

关键词：

Multiple criteria analysis; Preference disaggregation; Decision analysis; Linear programming; Non-monotonic value functions; PREFERENCE DISAGGREGATION; ORDINAL REGRESSION; UTILITY-FUNCTIONS; RANKING; SET; ELICITATION; SELECTION;

D O I：

10.1016/j.ejor.2016.11.038

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

摘要：

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 pair wise 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. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：1073 / 1084

页数：12

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