Evaluation of startup policies in simulation experiments

Wilson, James R.; Pritsker, A. Alan B.

doi:10.1177/003754977803100303

Cited by 87 publications

(32 citation statements)

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“…This is a problem that has challenged the simulation community for many years. Among the papers that have addressed this question are [9], [12], [30], [31], and [23]; see also [11] and [19]. It is probably safe to say that no technique yet proposed satisfactorily solves this problem.…”

Section: Introductionmentioning

confidence: 99%

Stationarity detection in the initial transient problem

Asmussen¹,

Glynn²,

Þórisson³

1992

ACM Trans. Model. Comput. Simul.

100

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Let X = {X(t)}>0 be a stochastic process with a stationary version X*. It is investigated when it is possible to generate by simulation a version JX of X with lower initial bias than X itself, in the sense that either XC is stationary (has the same distribution as X*) or the distribution of X is close to the distribution of X*. Particular attention is given to regenerative processes and Markov processes with a finite, countable or general state space. The results are both positive and negative, and indicate that the tail of the distribution of the cycle length r plays a critical role. The negative results essentially state that without some information on this tail, no apriori computable bias reduction is possible; in particular, this is the case for the class of all Markov processes with a countably infinite state space. On the contrary, the positive results give algorithms for simulating k for various classes of processes with some special structure on r, for example finite state Markov chains, Markov chains satisfying a Doeblin type minorization, and regenerative processes with r having a bounded (p + 1)th moment or having a stationary age distribution that can be generated by simulation.

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Section: Introductionmentioning

confidence: 99%

Stationarity detection in the initial transient problem

Asmussen¹,

Glynn²,

Þórisson³

1992

ACM Trans. Model. Comput. Simul.

100

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“…Gafarian et al (1978) and Wilson and Pritsker (1978a) review various truncation heuristics, and find that the methods available at that time are rather unsatisfactory. Wilson and Pritsker (1978b) state that choosing an initial state near the mode (rather than the mean) of the steadystate distribution produces favorable results. Another survey is provided by Chance (1993).…”

Section: Literature Reviewmentioning

confidence: 98%

Analysis of the behavior of the transient period in non-terminating simulations

Sandikçi¹,

Sabuncuoğlu²

2006

European Journal of Operational Research

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Computer simulation is a widely used tool for analyzing many industrial and service systems. However, a major disadvantage of simulation is that the results are only estimates of the performance measures of interest, hence they need careful statistical analyses. Simulation studies are often classified as either terminating or non-terminating. One of the major problems in non-terminating simulations is the problem of initial transient. Many techniques have been proposed in the literature to deal with this problem. There are currently a number of studies to improve the efficiency and effectiveness of these techniques. However, no research has been reported yet that analyzes the behavior of the transient period. In this paper, we investigate the factors affecting the length of the transient period for non-terminating simulations, particularly for serial production lines and job-shop production systems. Factors such as the variability of processing times, system size, existence of bottleneck, reliability of system, system load level, and buffer capacity are investigated. Recommendations for the use of a new technique are given. A comprehensive bibliography is also provided.

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“…For example, the crossing-of-the-mean rule (Fishman 1973, Wilson and Pritsker 1978a, 1978b was heavily criticised in the literature for being extremely sensitive to the selection of its main parameter, which was system-dependent, and misspecification of which caused significant over or under-estimation of the warm-up length (Pawlikowski 1990). This method was therefore rejected on ease of automation and accuracy grounds.…”

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confidence: 99%

Automating warm-up length estimation

Hoad

Robinson

Davies

2010

Journal of the Operational Research Society

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There are two key issues in assuring the accuracy of estimates of performance obtained from a simulation model. The first is the removal of any initialisation bias, the second is ensuring that enough output data is produced to obtain an accurate estimate of performance. This paper is concerned with the first issue, and more specifically warm-up estimation. Our aim is to produce an automated procedure, for inclusion into commercial simulation software, for estimating the length of warm-up and hence removing initialisation bias from simulation output data. This paper describes the extensive literature search that was carried out in order to find and assess the various existing warm-up methods, the process of short-listing and testing of candidate methods. In particular it details the extensive testing of the warm-up MSER-5 method.

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Evaluation of startup policies in simulation experiments

Cited by 87 publications

References 1 publication

Stationarity detection in the initial transient problem

Stationarity detection in the initial transient problem

Analysis of the behavior of the transient period in non-terminating simulations

Automating warm-up length estimation

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