We formulate and evaluate the Automated Simulation Analysis Procedure (ASAP), an algorithm for steady-state simulation output analysis based on the method of nonoverlapping batch means (NOBM). ASAP delivers a confidence interval for an expected response that is centered on the sample mean of a portion of a simulation-generated time series and satisfies a user-specified absolute or relative prec...

A batch machine is a machine that can process a number of jobs simultaneously as a batch, and the processing time of a batch is equal to the longest processing time of the jobs assigned to it. In this paper we present a polynomial time approximation scheme (PTAS) for scheduling a batch machine to minimize the total completion time with job release dates. Also, we present a fully polynomial time...

We study the problems of scheduling jobs, with different release dates and equal processing times, on two types of batching machines. All jobs of the same batch start and are completed simultaneously. On a serial batching machine, the length of a batch equals the sum of the processing times of its jobs and, when a new batch starts, a constant setup time s occurs. On a parallel batching machine,...

Batch means are sample means of subsets of consecutive subsamples from a simulation output sequence. Independent and normally distributed batch means are not only the requirement for constructing a confidence interval for the mean of the steady-state distribution of a stochastic process, but are also the prerequisite for other simulation procedures such as ranking and selection (R&S). We propos...

This paper deals with the design of observers for batch processes and the tuning of the observer gains with the objective to guarantee fast convergence of the state estimates. The approach followed in the present proposes to use an ITSE (Integral of the Time-weighted Square Error) criterion. The approach is illustrated on a batch process used in wastewater treatment, sequencing batch reactor (S...

We examine key convergence properties of the steady-state simulation analysis method of nonoverlapping batch means (NOBM) when it is applied to a stationary, phi-mixing process. For an increasing batch size and a fixed batch count, we show that the standardized vector of batch means converges in distribution to a vector of independent standard normal variates—a well-known result underlying the ...

We develop an automated wavelet-based spectral method for constructing an approximate confidence interval on the steady-state mean of a simulation output process. This procedure, called WASSP, determines a batch size and a warmup period beyond which the computed batch means form an approximately stationary Gaussian process. Based on the log-smoothed-periodogram of the batch means, WASSP uses wa...

Calculating a Monte Carlo standard error (MCSE) is an important step in the statistical analysis of the simulation output obtained from a Markov chain Monte Carlo experiment. An MCSE is usually based on an estimate of the variance of the asymptotic normal distribution. We consider spectral and batch means methods for estimating this variance. In particular, we establish conditions which guarant...