Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties

By Jianbo Yang. European Journal of Operational Research, 2001

786 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e270ec0eaaa002665ac229f9e0e2678ce51ad2e2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Dongling Xu
2. Hai Wang
3. Zeshui Xu
4. Qi-ying Pang
5. T. Stewart
6. Ian N. Durbach
7. Yingming Wang
8. Kwai-Sang Chin

In Control and Systems Engineering

1. Dongling Xu
2. T. Stewart
3. Ian N. Durbach
4. Yingming Wang
5. Kwai-Sang Chin
6. Hai Wang
7. Jin Wang
8. Jun Liu

In Management Science and Operations Research

1. Dongling Xu
2. Ian N. Durbach
3. Jianbo Yang
4. T. Stewart
5. Hai Wang
6. Zeshui Xu
7. Kwai-Sang Chin
8. Yingming Wang

Citations in the corpus

• The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties (2006, methodology): “In the development of the non-fuzzy evidential reasoning approach, an information transformation process was proposed ( Yang, 2001 ).”
• Probabilistic linguistic term sets in multi-attribute group decision making (2016, background): “Moreover, Yang and Xu [36, 37] considered partial ignorance as well (in which the probabilistic information is interpreted as the degrees of belief) under the framework of evidential reasoning.”
• Inference and learning methodology of belief-rule-based expert system for pipeline leak detection (2007, methodology): “The transformation is based on the rule-based transformation technique (Yang, 2001) for the quantitative data transformation.”
• Evidential reasoning rule for evidence combination (2013, methodology): “This research is also motivated to investigate the rationale and foundation of the ER approach, which was developed to support MCDA of a quantitative and qualitative nature under uncertainty by applying D–S theory [38,40,41,43], to generalise it for evidence combination in general.”
• On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty (2002, methodology): “[24] J. B. Yang and P. Sen, “A general multi-level evaluation process for hybrid MADM with uncertainty,” IEEE Trans.”
• Modeling uncertainty in multi-criteria decision analysis (2012, methodology): “These can be done using either rule-based or utility-based transformations [154].”