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    Neurofuzzy Genetic System for Selection of Construction Project Managers

    Source: Journal of Construction Engineering and Management:;2011:;Volume ( 137 ):;issue: 001
    Author:
    Abbas Rashidi
    ,
    Fateme Jazebi
    ,
    Ioannis Brilakis
    DOI: 10.1061/(ASCE)CO.1943-7862.0000200
    Publisher: American Society of Civil Engineers
    Abstract: Choosing a project manager for a construction project—particularly, large projects—is a critical project decision. The selection process involves different criteria and should be in accordance with company policies and project specifications. Traditionally, potential candidates are interviewed and the most qualified are selected in compliance with company priorities and project conditions. Precise computing models that could take various candidates’ information into consideration and then pinpoint the most qualified person with a high degree of accuracy would be beneficial. On the basis of the opinions of experienced construction company managers, this paper, through presenting a fuzzy system, identifies the important criteria in selecting a project manager. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy as well as the functions used by the fuzzy system to make initial estimates of the cluster centers for fuzzy c-means clustering. Moreover, a back-propagation neutral network method was used to train the system. The optimal measures of the inference parameters were identified by calculating the system’s output error and propagating this error within the system. After specifying the system parameters, the membership function parameters—which by means of clustering and projection were approximated—were tuned with the genetic algorithm. Results from this system in selecting project managers show its high capability in making high-quality personnel predictions.
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      Neurofuzzy Genetic System for Selection of Construction Project Managers

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    http://yetl.yabesh.ir/yetl1/handle/yetl/58352
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    contributor authorAbbas Rashidi
    contributor authorFateme Jazebi
    contributor authorIoannis Brilakis
    date accessioned2017-05-08T21:39:08Z
    date available2017-05-08T21:39:08Z
    date copyrightJanuary 2011
    date issued2011
    identifier other%28asce%29co%2E1943-7862%2E0000206.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/58352
    description abstractChoosing a project manager for a construction project—particularly, large projects—is a critical project decision. The selection process involves different criteria and should be in accordance with company policies and project specifications. Traditionally, potential candidates are interviewed and the most qualified are selected in compliance with company priorities and project conditions. Precise computing models that could take various candidates’ information into consideration and then pinpoint the most qualified person with a high degree of accuracy would be beneficial. On the basis of the opinions of experienced construction company managers, this paper, through presenting a fuzzy system, identifies the important criteria in selecting a project manager. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy as well as the functions used by the fuzzy system to make initial estimates of the cluster centers for fuzzy c-means clustering. Moreover, a back-propagation neutral network method was used to train the system. The optimal measures of the inference parameters were identified by calculating the system’s output error and propagating this error within the system. After specifying the system parameters, the membership function parameters—which by means of clustering and projection were approximated—were tuned with the genetic algorithm. Results from this system in selecting project managers show its high capability in making high-quality personnel predictions.
    publisherAmerican Society of Civil Engineers
    titleNeurofuzzy Genetic System for Selection of Construction Project Managers
    typeJournal Paper
    journal volume137
    journal issue1
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/(ASCE)CO.1943-7862.0000200
    treeJournal of Construction Engineering and Management:;2011:;Volume ( 137 ):;issue: 001
    contenttypeFulltext
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