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|Title:||AN ONLINE TRUNCATION METHOD FOR TRANSIENT BIAS REDUCTION IN STEADY STATE SIMULATION: DATA-DRIVEN ONLINE TRUNCATION METHOD (DOT)||Authors:||ZHANG NAN||Keywords:||Simulation, steady state, transient bias, online truncation methods||Issue Date:||25-Aug-2017||Citation:||ZHANG NAN (2017-08-25). AN ONLINE TRUNCATION METHOD FOR TRANSIENT BIAS REDUCTION IN STEADY STATE SIMULATION: DATA-DRIVEN ONLINE TRUNCATION METHOD (DOT). ScholarBank@NUS Repository.||Abstract:||In steady-state simulations, the conclusions of a steady-state performance are biased if transient bias remains in the simulation outputs. The transient bias is caused by an unrealistic initial condition. This thesis discusses one of the bias mitigation methods that reduces transient bias by deleting the biased observations’ phase, namely, truncation methods. Moreover, this thesis explores the truncation methods that can provide the real-time truncation position in an online simulation process. Herein, the online simulation is referred to as real-time dynamic running of the simulation model. Online truncation methods determine the truncation position in real time, saving the computer budget and consequently improving the simulation efficiency. However, there are few studies in this area, and existing online truncation methods usually underestimate the truncation positions. In this thesis, a new algorithm, the data-driven online truncation method (DOT), is proposed to provide accurate and reliable online truncation positions. The validity and efficiency of the proposed online approach are tested and verified with artificial data sets, M/M/1 queuing systems and serial production lines and are further compared with other existing online truncation methods. The results of the numerical tests show that this proposed algorithm is an effective and robust online truncation method.||URI:||http://scholarbank.nus.edu.sg/handle/10635/137756|
|Appears in Collections:||Master's Theses (Open)|
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