• Journal of the Korea Society for Simulation
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• v.22 no.3
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• pp.35-41
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• 2013

This paper applies an active period based bottleneck detection method to flow shop manufacturing system with limited buffer size. Manufacturing systems are constrained by one or more bottlenecks which degrades the system throughput. Conventional bottleneck detection methods include the waiting time or queue length of production stations and their utilization. Due to the random events such as production time of items, machine failure and repair times, the systems may change over time, and subsequently bottlenecks shift from one station to another station. Active period of working station may cause other stations to wait for productions. Information when and where active periods occur helps to find bottlenecks in production systems. Based on these informations, we predict bottlenecks in applying AweSim simulation language. We compare the simulation results of conventional methods with those obtained from duration of active period method, and duration ratio method of both sole and shift bottleneck periods. Even though simulation results are from simple flow shop model, they are quite promising for predicting bottlenecks of production stations. We hope this study aids in decision making regarding the improving system production yield and allocation of available resources of system.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1057-1067
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• 2014

Many genetic variants have been identified to be associated with complex diseases such as hypertension, diabetes and cancers throughout genome-wide association studies (GWAS). However, there still exist a serious missing heritability problem since the proportion explained by genetic variants from GWAS is very weak less than 10~15%. Gene-gene interaction study may be helpful to explain the missing heritability because most of complex disease mechanisms are involved with more than one single SNP, which include multiple SNPs or gene-gene interactions. This paper focuses on gene-gene interactions with the survival phenotype by extending the multifactor dimensionality reduction (MDR) method to the accelerated failure time (AFT) model. The standardized residual from AFT model is used as a residual score for classifying multiple geno-types into high and low risk groups and algorithm of MDR is implemented. We call this method AFT-MDR and compares the power of AFT-MDR with those of Surv-MDR and Cox-MDR in simulation studies. Also a real data for leukemia Korean patients is analyzed. It was found that the power of AFT-MDR is greater than that of Surv-MDR and is comparable with that of Cox-MDR, but is very sensitive to the censoring fraction.