Inferring an ELECTRE I model from binary outranking relations

Q4 Business, Management and Accounting

International Journal of Multicriteria Decision Making Pub Date : 2018-09-05 DOI:10.1504/IJMCDM.2018.10015565

H. Frikha, Sawsan Charfi

引用次数: 1

Abstract

Multi-criteria decision making (MCDM) consists in choosing, ranking or sorting alternatives belonging to a finite set and evaluated according to several and usually conflicting criteria. Criteria weights play a crucial role in the decision process since they express the importance of the considered criteria as seen by the decision maker (DM). However, these weight values assigned directly by the DM are subjective and little reliable as they are based only on his experience, his intuition and his psychological state. To overcome the subjectivity problem, several methods are developed to infer criteria weights from DM judgments. The aim of this paper is to develop an approach eliciting ELECTRE I criteria weights using binary outranking relations furnished by the decision-maker and taking support on mathematical programming.

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从二元超排序关系推断出ELECTRE I模型

多准则决策(MCDM)包括选择、排序或排序属于有限集合的备选方案，并根据几个通常相互冲突的标准进行评估。标准权重在决策过程中起着至关重要的作用，因为它们表达了决策者(DM)所看到的所考虑的标准的重要性。然而，DM直接分配的这些权重值是主观的，不太可靠，因为它们只是基于他的经验、直觉和心理状态。为了克服主观性问题，提出了几种从决策决策判断中推断准则权重的方法。本文的目的是在数学规划的支持下，利用决策者提供的二元超排序关系，开发一种获取ELECTRE I标准权重的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Multicriteria Decision Making Business, Management and Accounting-Strategy and Management

CiteScore

0.70

自引率

0.00%

发文量

期刊介绍： IJMCDM is a scholarly journal that publishes high quality research contributing to the theory and practice of decision making in ill-structured problems involving multiple criteria, goals and objectives. The journal publishes papers concerning all aspects of multicriteria decision making (MCDM), including theoretical studies, empirical investigations, comparisons and real-world applications. Papers exploring the connections with other disciplines in operations research and management science are particularly welcome. Topics covered include: -Artificial intelligence, evolutionary computation, soft computing in MCDM -Conjoint/performance measurement -Decision making under uncertainty -Disaggregation analysis, preference learning/elicitation -Group decision making, multicriteria games -Multi-attribute utility/value theory -Multi-criteria decision support systems and knowledge-based systems -Multi-objective mathematical programming -Outranking relations theory -Preference modelling -Problem structuring with multiple criteria -Risk analysis/modelling, sensitivity/robustness analysis -Social choice models -Theoretical foundations of MCDM, rough set theory -Innovative applied research in relevant fields