مدل سازی ریسک زمان بندی در تولید مسکن پیش ساخته
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|151959||2017||15 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Cleaner Production, Volume 153, 1 June 2017, Pages 692-706
Every country is dealing with its own housing problems; however, none compares with Hong Kong where housing has always been a major concern as a result of low supply over the past decades. Against the constraints in delivering sufficient houses, prefabrication as a sustainable solution for housing has been increasingly advocated for its potential merits of better quality, construction safety and cleaner built environment. However, schedule delay caused by various risks affected the prefabrication housing production (PHP) in Hong Kong. This problem can be further worsened when the manufacturing sector of PHP has entirely moved to offshore areas in the Pearl River Delta region. This study applies system dynamics to recognize and investigate the potential effect of various risks on the scheduling of prefabrication housing construction projects through the employment of Vensim software package. The simulation results show that schedule risks, namely low information interoperability between different enterprise resource planning systems (LIIBDERPS), logistics information inconsistency due to human errors probability (LIIHEP), Delay of delivery of precast element to site (DDPES), and Design information gap between designer and manufacturer (DIGDM) significantly contribute to the schedule delay in PHP. However, schedule is more sensitive toward LIIBDERPS than for the other three risks, indicating that LIIBDERPS should be monitored and given priority. The system dynamic model serves as an effective tool for quantitatively evaluating the effect of various risks on the schedule of PHP, offering valuable references for managers though comparing simulation results under different risk scenarios, so that potential risks that might lead to schedule delay could be identified and handled in advance.