Regenerative Simulation

Shedler, Gerald S.

doi:10.1007/978-1-4612-1050-4_2

Cited by 24 publications

(36 citation statements)

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“…Therefore, in our current model we follow Brinksma and Mader [5], who use the well-known variable time advance procedure [21]. With a variable time advance procedure, simulated time goes forward to the next moment in time at which some event triggers a state transition, and all intervening time is skipped.…”

Section: Personalisation Machinementioning

confidence: 99%

Optimal Scheduling Using Branch and Bound with SPIN 4.0

Ruys

2003

Model Checking Software

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Abstract. The use of model checkers to solve discrete optimisation problems is appealing. A model checker can first be used to verify that the model of the problem is correct. Subsequently, the same model can be used to find an optimal solution for the problem. This paper describes how the new Promela primitives of Spin 4.0 can be applied to search effectively for the optimal solution. We show how Branch-and-Bound techniques can be added to the LTL property that is used to find the solution. The LTL property is dynamically changed during the verification. We also show how the syntactical reordering of statements and/or processes in the Promela model can improve the search even further. The techniques are illustrated using two running examples: the Travelling Salesman Problem and a job-shop scheduling problem.

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Section: Personalisation Machinementioning

confidence: 99%

Optimal Scheduling Using Branch and Bound with SPIN 4.0

Ruys

2003

Model Checking Software

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“…Our interest is not restricted to propose point estimation procedures, but also to assess the precision of the point estimators through an asymptotic confidence interval [14].…”

Section: Mathematical Frameworkmentioning

confidence: 99%

A Bayesian Framework for the Incorporations of Priors and Sample Data in Simulation Experiments

Muñoz

2008

TOORJ

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Abstract:In this article, we propose a theoretical framework to estimate performance measures in simulation experiments, incorporating both sample data from a random component and priors on input parameters of the simulation model. Our approach takes into account both the inherent uncertainty of the model as well as parameter uncertainty. We discuss the estimation of a conditional expectation under a Bayesian framework and point and variability estimators are proposed when direct sampling from the posterior distribution is not allowed. The application and properties of the proposed methodology are illustrated through an inventory model and simulation experiments using a Markovian model.

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“…It is well known that confidence intervals based an different types of regeneration points are asymptotically equivalent, [28], [34], [35]. Nevertheless, the difference in the number of regeneration points of different types may be crucial for efficiency of simulation procedure.…”

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confidence: 98%

On steady‐state simulation of some weak regenerative networks

Morozov

Aminova

2002

Trans Emerging Tel Tech

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Abstract. We discuss the possibilities of the so-called weak regeneration in the steady-state analysis of multiserver queues and some Jackson-type and multiclass queueing networks when classical regenerative simulation is ineffective. A few constructions of weak regeneration for open Jackson-type and multiclass queueing networks are given in an explicit form. Conditions are found when efficiency of weak regeneration (expressed in the simulation time needed for confidence estimation with a given precision) stays very high while the efficiency of classical regeneration is rapidly decreasing. Numerical results are also considered which illustrate an advantage of the weak regeneration in simulation of some tandem queueing networks.

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Regenerative Simulation

Cited by 24 publications

References 0 publications

Optimal Scheduling Using Branch and Bound with SPIN 4.0

Optimal Scheduling Using Branch and Bound with SPIN 4.0

A Bayesian Framework for the Incorporations of Priors and Sample Data in Simulation Experiments

On steady‐state simulation of some weak regenerative networks

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