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김률희(Kim Ryul-Hee),배태현(Bae Tae-Hyun),이동은(Lee Dong-Eun) 대한건축학회 2009 大韓建築學會論文集 : 構造系 Vol.25 No.5
This paper introduces an automated tool named Advanced Stochastic Simulation-based Scheduling system(AS3). The system automatically integrates CPM schedule data exported from P3 into it and computes the best fit Probability Distribution Functions (PDFs) of historical activity duration data. In addition, it defines activity durations using the PDFs identified, simulate the schedule network, and estimates the best fit PDF of project completion times (PCTs). AS3 integrates the automated best fitting function which identifies the exact distribution of PCTs using the “goodness of fit” principles into existing simulation-based scheduling method. It improves the reliability of simulation-based scheduling by effectively dealing the uncertainties of the activity durations rather than existing systems dose. Furthermore, it increases the usability of the schedule data obtained from commercial CPM softwares, and effectively handle the variability of the PCTs by finding the best fit PDF of PCTs. It is implemented as an easy-to-use computerized tool programmed in MATLAB. AS3 can make significant contribute to the field of simulation-based scheduling, because (1) it analyzes the effect of different distributions of activity durations on the distribution of the PCTs, (2) the reliability obtained by the system is higher than conventional simulation-based scheduling systems, (3) simplifies the tedious and burdensome process involved in finding the PDFs of the many activity durations, and (4) it is a welcome replacement for the normality assumptions used by most simulation-based scheduling researchers, and therefore increase the usability of simulation-based scheduling and generates more accurate results.
시뮬레이션을 基盤으로 하는 투찰율 推定(대한주택공사 전자입찰 공시자료를 사용한 사례연구)
임태경,김률희,이창용,이동은 대한건축학회지회연합회 2007 대한건축학회지회연합회 학술발표대회논문집 Vol.2007 No.1
This paper introduces a system called Simulation-based Stochastic Bidding Cost Estimation System (S2BC) that use simulation modeling and analysis technique. It better represents the real world system involved in construction bidding. The prediction accuracy was compromised because of the assumptions which existing models have adopted. However, The S2BC complements the lack of accuracy. Historical bidding data obtained from the Korea National Housing Corporation was used to validate the method. Bidding projects in the Historical DB were selected by random sampling. The ration of bidding let was calculated using the occurrence of participants who entered into a specific bidding. On the assumption that bidding pattern retained on the historical DB has reproducibility, the probability of winning was computed using the cumulative probability distribution that is obtained from the user defined numbe r of random sampling of bidding projects out of the Historical DB. The existing computation, which is handled by means of deterministic procedure, was converted into stochastic model using simulation modeling and analysis technique. The best fitted probability distribution function is estimated using the historical data. The reliability of estimating probability of winning on specified bidding let is improved using the S2BC.