Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/6775
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dr. KWOK Pak Ki, Alex | en_US |
dc.contributor.author | Lau, Y. K. Henry | en_US |
dc.date.accessioned | 2021-11-26T08:05:40Z | - |
dc.date.available | 2021-11-26T08:05:40Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Journal of Industrial and Intelligent Information, Jan. 2016, vol. 4(1), pp. 76-82. | en_US |
dc.identifier.issn | 2301-3745 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11861/6775 | - |
dc.description.abstract | Group decision making is an important symbol of a democratic society. It helps to explore problems from wider angles and the results generated will usually be widely accepted by the public. Yet, there are drawbacks of group decision making. It usually takes more time and resources than individual decision making and also sometimes the one with a louder voice will dominate the discussion. In this paper, a group decision making framework derived that are based on the modified Delphi-AHP method based on minimum-cost consensus model (MCCM) and vague set theory is proposed to gather different experts’ opinions on the evaluation criteria and their relative importance for choosing a suitable road junction control method in a multi-objectives environment. It is believed that the proposed framework can help to strengthen the advantages and to solve the above drawbacks of group decision making. A numerical case study is proposed to demonstrate the use of this framework. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Journal of Industrial and Intelligent Information | en_US |
dc.title | Modified delphi-AHP method based on minimum-cost consensus model and vague set theory for road junction control method evaluation criteria | en_US |
dc.type | Other Article | en_US |
dc.identifier.doi | 10.12720/jiii.4.1.76-82 | - |
crisitem.author.dept | Department of Applied Data Science | - |
item.fulltext | No Fulltext | - |
Appears in Collections: | Applied Data Science - Publication |
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