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A Fuzzy Consensus Building Framework...
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Elbarkouky, Mohamed Mohamed Galal.
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A Fuzzy Consensus Building Framework for Early Alignment of Construction Project Teams on the Extent of their Roles and Responsibilities.
Record Type:
Language materials, printed : Monograph/item
Title/Author:
A Fuzzy Consensus Building Framework for Early Alignment of Construction Project Teams on the Extent of their Roles and Responsibilities./
Author:
Elbarkouky, Mohamed Mohamed Galal.
Description:
422 p.
Notes:
Source: Dissertation Abstracts International, Volume: 72-01, Section: A, page: 0270.
Contained By:
Dissertation Abstracts International72-01A.
Subject:
Psychology, Social. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR67638
ISBN:
9780494676387
A Fuzzy Consensus Building Framework for Early Alignment of Construction Project Teams on the Extent of their Roles and Responsibilities.
Elbarkouky, Mohamed Mohamed Galal.
A Fuzzy Consensus Building Framework for Early Alignment of Construction Project Teams on the Extent of their Roles and Responsibilities.
- 422 p.
Source: Dissertation Abstracts International, Volume: 72-01, Section: A, page: 0270.
Thesis (Ph.D.)--University of Alberta (Canada), 2010.
This thesis presents a Fuzzy Consensus Building Framework (FCBF), which enables construction project parties to align their teams on their roles and responsibilities early on in their projects. The framework introduces a model that (1) incorporates consensus of construction project teams in aggregating their opinions to decide on the party responsible for every standard task of a construction project; (2) classifies the quality of experts in the decision making process by weighting their responses during aggregation, based on their attributes; and (3) resolves residual conflicts between project teams on their perceived shared tasks, using a consensus reaching process. A template of project and construction management tasks is extracted from relevant standard guidelines and interviews with industry peers. Different extents of the roles and responsibilities of the owner and contractors are described using seven linguistic terms. A modified similarity aggregation method (SAM) aggregates experts' opinions in a linguistic framework, using a consensus weight factor for each expert. A fuzzy expert system (FES) determines an importance weight factor for each expert, representing expert quality; opinions are aggregated using this factor and the consensus weight factor. Based on the aggregated opinions of experts, the tasks are classified into three responsibility lists: the owner's, the contractors', and the shared responsibility list. The fuzzy preference relations consensus (FPRC) approach is applied to the tasks of shared responsibility, and a linguistic consensus measure is applied to resolve potential conflicts between team members on their perceived shared tasks. Using a case study approach, the FCBF is applied to aid a project owner organization in the field of oil and gas to determine its roles and responsibilities in a customized project delivery system, called owner managing contractor (OMC). The FCBF contributes to the construction industry by solving a fundamental problem for project owners: it helps identify and reduce potential conflicts over the extent of project teams' responsibilities prior to the construction stage. It also provides an improvement over previous consensus-based approaches, which rely on a subjective assessment of experts' importance weights in aggregating their opinions, and it modifies the SAM to adapt it to a linguistic environment.
ISBN: 9780494676387Subjects--Topical Terms:
529430
Psychology, Social.
A Fuzzy Consensus Building Framework for Early Alignment of Construction Project Teams on the Extent of their Roles and Responsibilities.
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Source: Dissertation Abstracts International, Volume: 72-01, Section: A, page: 0270.
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Thesis (Ph.D.)--University of Alberta (Canada), 2010.
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This thesis presents a Fuzzy Consensus Building Framework (FCBF), which enables construction project parties to align their teams on their roles and responsibilities early on in their projects. The framework introduces a model that (1) incorporates consensus of construction project teams in aggregating their opinions to decide on the party responsible for every standard task of a construction project; (2) classifies the quality of experts in the decision making process by weighting their responses during aggregation, based on their attributes; and (3) resolves residual conflicts between project teams on their perceived shared tasks, using a consensus reaching process. A template of project and construction management tasks is extracted from relevant standard guidelines and interviews with industry peers. Different extents of the roles and responsibilities of the owner and contractors are described using seven linguistic terms. A modified similarity aggregation method (SAM) aggregates experts' opinions in a linguistic framework, using a consensus weight factor for each expert. A fuzzy expert system (FES) determines an importance weight factor for each expert, representing expert quality; opinions are aggregated using this factor and the consensus weight factor. Based on the aggregated opinions of experts, the tasks are classified into three responsibility lists: the owner's, the contractors', and the shared responsibility list. The fuzzy preference relations consensus (FPRC) approach is applied to the tasks of shared responsibility, and a linguistic consensus measure is applied to resolve potential conflicts between team members on their perceived shared tasks. Using a case study approach, the FCBF is applied to aid a project owner organization in the field of oil and gas to determine its roles and responsibilities in a customized project delivery system, called owner managing contractor (OMC). The FCBF contributes to the construction industry by solving a fundamental problem for project owners: it helps identify and reduce potential conflicts over the extent of project teams' responsibilities prior to the construction stage. It also provides an improvement over previous consensus-based approaches, which rely on a subjective assessment of experts' importance weights in aggregating their opinions, and it modifies the SAM to adapt it to a linguistic environment.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR67638
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