Interpolation approximations of sojourn time distributions
Operations Research
Determining job completion time distributions in stochastic production environments
WSC '95 Proceedings of the 27th conference on Winter simulation
Proceedings of the 35th conference on Winter simulation: driving innovation
Proceedings of the 35th conference on Winter simulation: driving innovation
The accuracy of a new confidence interval method
WSC '04 Proceedings of the 36th conference on Winter simulation
Variance-based sampling for cycle time: throughput confidence intervals
WSC '04 Proceedings of the 36th conference on Winter simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Bootstrapping simultaneous confidence bands
WSC '05 Proceedings of the 37th conference on Winter simulation
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A common problem in production environments is the need to estimate the remaining time in system for work-in-progress jobs. Simulation can be used to obtain the estimates. However, when the future path of a job is uncertain (due to stochastic events such as rework), using simulation to estimate the remaining cycle time of a job at step k can be imprecise; traditional confidence intervals on the estimated remaining cycle times may be too large to be of practical significance. We propose a response surface methodology-based approach to estimating conditional confidence intervals on the remaining cycle times as jobs progress through the system and more information is obtained on them. This method will provide more useful and accurate estimates of remaining cycle times at various stages of the process flow. Further, we outline two different simulation approaches for estimating the response surfaces used to generate the confidence intervals.