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ﺗﻮﺳﻌﻪ ﻣﺪل ﺗﻠﻔﯿﻘﯽ اﺳﺘﻨﺘﺎج ﻓﺎزی و اﻧﻔﯿﺲ ﺟﻬﺖ ﺗﺤﻠﯿﻞ و ﻣﺪﯾﺮﯾﺖ رﯾﺴﮏ ﻣﻨﺎﺑﻊ اﻧﺴﺎﻧﯽ در ﭘﺮوژهﻫﺎی ﻋﻤﺮاﻧﯽ
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نویسنده
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قلی زاده مرتضی ,فرد مرادی نیا سینا
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منبع
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مهندسي سازه و ساخت - 1401 - دوره : 9 - شماره : 11 - صفحه:140 -164
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چکیده
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ﻧﻘﺶ ﻣﻨﺎﺑﻊ اﻧﺴﺎﻧﯽ در ﮐﺎﻫﺶ رﯾﺴﮏﻫﺎی ﻣﻨﺎﺑﻊ اﻧﺴﺎﻧﯽ و ﺑﻪ ﺗﺒﻊ آن رﯾﺴﮏﻫﺎی ﺳﺎزﻣﺎﻧﯽ و ﺳﻮدآوری ﺳﺎزﻣﺎن، ﻧﻘﺸﯽ ﻏﯿﺮ ﻗﺎﺑﻞ اﻧﮑﺎر و ﺑﺴﯿﺎر ﻣﻬﻢ اﺳﺖ و ﻧﻘﺶ ﻣﻨﺎﺑﻊ اﻧﺴﺎﻧﯽ ﭼﯿﺰی ﻓﺮاﺗﺮ از ﻧﻘﺶ ﻫﺎی اداری و دﻓﺘﺮی ﻣﯽﺑﺎﺷﺪ ﮐﻪ ﻣﺘﺄﺳﻔﺎﻧﻪ اﯾﻦ دﯾﺪﮔﺎه راﺟﻊ ﺑﻪ ﻧﻘﺶ ﻣﺪﯾﺮﯾﺖ ﻣﻨﺎﺑﻊ اﻧﺴﺎﻧﯽ در اﮐﺜﺮ ﺳﺎزﻣﺎنﻫﺎی ﭘﺮوژه ﻣﺤﻮر وﺟﻮد دارد و ﺗﺎﮐﻨﻮن رﯾﺴﮏ اﻗﺪاﻣﺎت اﺳﺘﺮاﺗﮋﯾﮏ ﻣﻨﺎﺑﻊ اﻧﺴﺎﻧﯽ، ﺑﻪ ﺧﺼﻮص در ﭘﺮوژه ﻫﺎی ﺻﻨﻌﺖ ﺳﺎﺧﺖ ﮐﺸﻮر ﺑﻪ ﺻﻮرت ﻧﻈﺎم ﻣﻨﺪ ارزﯾﺎﺑﯽ ﻧﺸﺪه اﺳﺖ. ﺗﺤﻘﯿﻖ ﺣﺎﺿﺮ ﺑﺎ ﻫﺪف ﺷﻨﺎﺳﺎﯾﯽ رﯾﺴﮏﻫﺎی ﻣﻮﺛﺮ ﺑﺮ ﻣﺪﯾﺮﯾﺖ ﻣﻨﺎﺑﻊ اﻧﺴﺎﻧﯽ در ﭘﺮوژهﻫﺎی ﻋﻤﺮاﻧﯽ و اراﺋﻪ اﻟﮕﻮﯾﯽ ﯾﮑﭙﺎرﭼﻪ ﻣﺒﺘﻨﯽ ﺑﺮ ﺳﯿﺴﺘﻢ اﺳﺘﻨﺘﺎج ﻓﺎزی- ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﺗﻄﺒﯿﻘﯽ )anfis ( ﺟﻬﺖ ارزﯾﺎﺑﯽ رﯾﺴﮏﻫﺎ ﺑﻪ ﻧﮕﺎرش درآﻣﺪه اﺳﺖ. ﺑﺎ ﺗﻌﯿﯿﻦ ﻣﯿﺰان اﺣﺘﻤﺎل، ﺷﺪت اﺛﺮ و ﻧﺮخ وﻗﻮع رﯾﺴﮏﻫﺎ، در ﻣﺠﻤﻮع 9 ﻋﺎﻣﻞ رﯾﺴﮏ ﺷﺎﻣﻞ ﮐﻤﺒﻮد و ﻓﻘﺪان ﺑﻠﻮغ و داﻧﺶ ﮐﺎرﮐﻨﺎن ﻣﺘﺨﺼﺺ، ﺗﻨﺶﻫﺎی ﺷﻐﻠﯽ، ﺑﯽاﻧﮕﯿﺰﮔﯽ ﮐﺎرﮐﻨﺎن، ﻋﺪم ﺗﻮﺟﻪ ﺑﻪ ﻓﺮآﯾﻨﺪ ﻣﺪﯾﺮﯾﺖ ﻋﻤﻠﮑﺮد و اراﺋﻪ ﺑﺎزﺧﻮرد، ﮐﻤﺒﻮد ﻣﻨﺎﺑﻊ ﻣﺎﻟﯽ ﺑﺮای ﺗﺨﺼﯿﺺ ﭘﺎداش ﺑﻪ ﻓﻌﺎﻟﯿﺖﻫﺎی ﻧﻮآوراﻧﻪ، اﺗﺨﺎذ ﺳﯿﺎﺳﺖﻫﺎی ﭘﺮداﺧﺖ ﻧﺎﻣﻨﺎﺳﺐ، ﻋﻤﻠﮑﺮد ﺗﺒﻌﯿﺾآﻣﯿﺰ ﺑﯿﻦ ﻣﻨﺎﺑﻊ اﻧﺴﺎﻧﯽ، ﻋﺪم ﺑﺮﺧﻮرداری از داﻧﺶ و ﻣﻬﺎرتﻫﺎی ﻓﻨﯽ و ﻋﺪم ﺑﺮﺧﻮرداری از ﻣﻬﺎرت اﻋﺘﻤﺎدﺳﺎزی ﺑﯿﻦ ﮐﺎرﮐﻨﺎن ﺑﻪﻋﻨﻮان رﯾﺴﮏﻫﺎی ﺑﺤﺮاﻧﯽ )ﻏﯿﺮﻣﺠﺎز( ﺷﻨﺎﺳﺎﯾﯽ ﺷﺪ. ﻧﺘﺎﯾﺞ ﻣﻌﯿﺎرﻫﺎی ﺿﺮﯾﺐ ﻫﻤﺒﺴﺘﮕﯽ ﺟﻬﺖ ﺗﻌﯿﯿﻦ ﻣﻘﺪار ﺧﻄﺎی دو روش اراﺋﻪ ﺷﺪه ﻧﺸﺎن داد ﮐﻪ دادهﻫﺎی ﺗﺼﺎدﻓﯽ ﻧﺴﺒﺖ ﺑﻪ دادهﻫﺎی واﻗﻌﯽ ﺧﺒﺮﮔﺎن، ﺿﺮﯾﺐ ﻫﻤﺒﺴﺘﮕﯽ ﺑﯿﺸﺘﺮی داﺷﺘﻪ و ﻧﺘﺎﯾﺞ ﺣﺎﺻﻞ از آﻧﻬﺎ ﺑﺎ دﻗﺖ ﺑﺎﻻﯾﯽ رﺿﺎﯾﺖﺑﺨﺶ اﺳﺖ. ﻫﻤﭽﻨﯿﻦ ﺗﺤﻠﯿﻞ ﮐﯿﻔﯽ و ﮐﻤﯽ رﯾﺴﮏﻫﺎ ﺑﺎ روش اﻧﻔﯿﺲ و ﺑﺮاﺳﺎس ﻧﻈﺮات ﺧﺒﺮﮔﺎن و دادهﻫﺎی ﺗﺼﺎدﻓﯽ ﻧﺸﺎن داد ﮐﻪ اﮔﺮ رﯾﺴﮏﻫﺎ در ﭘﺮوژهﻫﺎﯾﯽ ﮐﻪ ﺑﺮای اوﻟﯿﻦ ﺑﺎر اﺟﺮا ﻣﯽﺷﻮﻧﺪ ﯾﺎ دﺳﺘﺮﺳﯽ ﺑﻪ ﺧﺒﺮﮔﺎن ﺑﻪ ﺗﻌﺪاد ﻣﻮرد ﻧﯿﺎز ﻣﻮﺟﻮد ﻧﺒﺎﺷﺪ، ﺑﺎ اﺗﺨﺎذ و اﻧﺘﺨﺎب دادهﻫﺎی ورودی ﺗﺼﺎدﻓﯽ ﻣﻨﺎﺳﺐ از ﺑﯿﻦ ﺑﺎزهﻫﺎی ﻓﺎزی ﺑﻪ ﺟﺎی ﻧﻈﺮ ﺧﺒﺮﮔﺎن و ﺗﺤﻠﯿﻞ آﻧﻬﺎ ﺑﺎ روش اﻧﻔﯿﺲ، ﻧﺘﺎﯾﺞ ﻗﺎﺑﻞ ﻗﺒﻮﻟﯽ ﺣﺎﺻﻞ ﻣﯽﮔﺮدد.
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کلیدواژه
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ﺗﺤﻠﯿﻞ رﯾﺴﮏ، رﯾﺴﮏ ﻣﻨﺎﺑﻊ اﻧﺴﺎﻧﯽ، ﭘﺮوژهﻫﺎی ﻋﻤﺮاﻧﯽ، ﻣﺪل اﻧﻔﯿﺲ، ﻣﺘﻠﺐ
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آدرس
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دانشگاه آزاد اسلامی واحد تبریز, گروه مهندسی عمران, ایران, دانشگاه آزاد اسلامی واحد تبریز, دانشکده فنی و مهندسی, ایران
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پست الکترونیکی
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fardmoradinia@iaut.ac.ir
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development an anfis model for human resource risk analysis and management in construction projects
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Authors
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gholizadeh morteza ,fard moradinia sina
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Abstract
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the role of human resources in reducing human resource risks and, consequently, organizational risks and organizational profitability, is an undeniable and very important role, and the role of human resources is something beyond administrative and office roles, which unfortunately this view there is a role for human resource management in most project-oriented organizations and so far the risk of strategic human resource actions, especially in the country’s manufacturing industry projects has not been systematically assessed.the present paper aimed to identify the risks affecting human resource management in construction projects and provide an integrated model based on adaptive network-based fuzzy inference system (anfis) for risk assessment. by determining the probability, severity and rate of occurrence of risks, a total of 9 risk factors including (1) lack of maturity and knowledge of specialized staff, (2) job stress, (3) employee motivation, (4) lack of attention to the management process performance and feedback, (5) lack of financial resources to reward innovative activities, (6) inappropriate payment policies, (7) discriminatory performance between human resources, (8) lack of technical knowledge and skills, and (9) lack of possession of trust-building skills among employees was identified as critical (unauthorized) risks. the results of correlation coefficient criteria to determine the error value of the two methods presented showed that random data had a higher correlation coefficient than the actual data of experts and the results were satisfactory with high accuracy. also, qualitative and quantitative analysis of risks by anfis method and based on experts’ opinions and random data showed that if the risks are not available in projects that are being implemented for the first time or access to the required number of experts, by adopting and selecting appropriate random input data. fuzzy intervals, instead of experts’ opinions and their analysis by anfis method, obtain acceptable results.
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