A Three-Stage Hybrid Approach for Weight Assignment in MADM

Guo-liang Yang, Jian-Bo Yang, Dong Xu, Mohammad Khoveyni

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Abstract

How to determine weights for attributes is one of the key issues in multiple attribute decision making (MADM). This paper aims to investigate a new approach for determining attribute weights s based on a data envelopment analysis (DEA) model without explicit inputs (DEA-WEI) and minimax reference point optimisation. This new approach first considers a set of preliminary weights and the most favourite set of weights for each alternative or decision making unit (DMU) and then aggregates these weight sets to find the best compromise weights for attributes with the interests of all DMUs taken into account fairly and simultaneously. This approach is intended to support the solution of such MADM problems as performance assessment and policy analysis where (a) the preferences of decision makers (DMs) are either unclear and partial or difficult to acquire and (b) there is a need to consider the best "will" of each DMU. Two case studies are conducted to show the property of this new proposed approach and how to use it to determine weights for attributes in practice. The first case is about the assessment of research strengths of EU-28 member countries under certain measures and the second is for analysing the performances of Chinese Project 985 universities, where the weights of the attributes need to be assigned in a fair and unbiased manner.
Original languageEnglish
Pages (from-to)93-105
JournalOmega (United Kingdom)
Volume71
Early online date3 Oct 2016
DOIs
Publication statusPublished - 2017

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