Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/6753
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 | 2021-11-19T06:38:05Z | - |
dc.date.available | 2021-11-19T06:38:05Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE Access, 2018, vol. 6, pp. 3509-3523. | en_US |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11861/6753 | - |
dc.description | Open access | en_US |
dc.description.abstract | This paper presents a methodology for building a consensus among the stakeholders based on the level of importance of each evaluation criterion to choose the best final design, approach, or solution for a project. The proposed methodology adopts an improved vague set minimum-cost consensus model (MCCM) and a modified Delphi method. This paper discusses several shortcomings of the traditional MCCM and the Delphi method. It argues that humans' resistance to change based on a numerical rating scale follows a natural logarithm pattern instead of a linear or quadratic pattern as claimed in other studies. Hence, the traditional MCCM needs to be revised accordingly. In addition, an experiment was concluded to compare the proposed consensus-building methodology with the traditional Delphi method. The experiment showed that the proposed methodology is better than the traditional Delphi method regarding using fewer rounds to reach a consensus. At last, this paper applies the proposed methodology to the field of road junction design selection. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | IEEE Access | en_US |
dc.title | A modified consensus-building methodology for reaching a group decision using minimum costs | en_US |
dc.type | Peer Reviewed Journal Article | en_US |
dc.identifier.doi | 10.1109/ACCESS.2017.2787677 | - |
crisitem.author.dept | Department of Applied Data Science | - |
item.fulltext | No Fulltext | - |
Appears in Collections: | Applied Data Science - Publication |
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