An experimental procedure for simulation response surface model identification
Communications of the ACM
Design of frequency-domain experiments for discrete-valued factors
Applied Mathematics and Computation
Driving frequency selection for frequency domain simulation experiments
Operations Research
Variance and bias reduction techniques for the harmonic gradient estimator
Applied Mathematics and Computation
Simulation factor screening using harmonic analysis
Management Science
Simulation sensitivity analysis: A frequency domain approach
WSC '81 Proceedings of the 13th conference on Winter simulation - Volume 2
SIAM Journal on Discrete Mathematics
BubbleSearch: a simple heuristic for improving priority-based greedy algorithms
Information Processing Letters
Design and Analysis of Experiments
Design and Analysis of Experiments
Lot streaming for product assembly in job shop environment
Robotics and Computer-Integrated Manufacturing
Robotics and Computer-Integrated Manufacturing
A systematic modelling and simulation approach for JIT performance optimisation
Robotics and Computer-Integrated Manufacturing
Efficient data allocation for frequency domain experiments
Operations Research Letters
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Dynamic events such as machine breakdown and hot jobs may induce problems on the production system such as order delay, increasing machine load, and changing inventory level. Past studies of dynamic events often use traditional design of experiments (DOE) to analyze the effects of dynamic events on system's performance. The shortcoming of this approach is that the number of experimental runs conducted would become exponentially increased as the number of factors increased. This study tries to use frequency domain methodology (FDM) instead so as to detect the higher order effects and rank important factors in a few experimental runs. Spectrum analysis is used to comprehend the effects of different location of machine breakdown and different size of hot jobs on the system's performance of flowshops with different traffic (utilization) and stability (oscillation). This study finds that the important factors identified by the FDM analysis are the same as that of DOE. However, only in some cases can the rankings of important factors be the same for both approaches. The dissimilarity between rankings of important factors found by these two methods is further measured using Kendall tau distance.