Paper Info
Title
Evidential reasoning based preference programming for multiple attribute decision analysis under uncertainty
Abstract
Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modelled and analysed using the evidential reasoning (ER) approach. Several types of uncertainty such as ignorance and fuzziness can be consistently modelled in the ER framework. In this paper, both interval weight assignments and interval belief degrees are considered, which could be incurred in many decision situations such as group decision making. Based on the existing ER algorithm, several pairs of preference programming models are constructed to support global sensitivity analysis based on the interval values and to generate the upper and lower bounds of the combined belief degrees for distributed assessment and also the expected values for ranking of alternatives. A post-optimisation procedure is developed to identify non-dominated solutions, examine the robustness of the partial ranking orders generated, and provide guidance for the elicitation of additional information for generating more desirable assessment results. A car evaluation problem is examined to show the implementation process of the proposed approach.
Year
DOI
Venue
2007
10.1016/j.ejor.2006.09.064
European Journal of Operational Research
Keywords
Field
DocType
Multiple attribute decision analysis,The evidential reasoning approach,Uncertainty modelling,Interval evaluation,Non-linear optimization
Decision analysis,Ranking,Programming paradigm,Belief structure,Decision support system,Artificial intelligence,Evidential reasoning approach,Interval arithmetic,Machine learning,Mathematics,Group decision-making
Journal
Volume
Issue
ISSN
182
3
0377-2217
Citations 
PageRank 
References 
42
1.42
16
Authors
4
Name
Order
Citations
PageRank
Min Guo1853.74
Jian-Bo Yang23832203.05
Kwai-Sang Chin3103354.69
Hongwei Wang452830.86