Normal view MARC view

Reducing parameter uncertainty for stochastic systems

Author: Hui Ng, Szu ; Chick, StephenINSEAD Area: Technology and Operations ManagementIn: ACM Transactions on Modeling and Computer Simulation, vol. 16, no. 1, January 2006 Language: EnglishDescription: p. 26-51.Type of document: INSEAD ArticleNote: Please ask us for this itemAbstract: The design of many production and service systems is informed by stochastic model analysis. But the parameters of statistical distributions of stochastic models are rarely known with certainty, and are often estimated from field data. Even if the mean system performance is a known function of the model's parameters, there may still be uncertainty about the mean performance because the parameters are not known precisely. Several methods have been proposed to quantify this uncertainty, but data sampling plans have not yet been provided to reduce parameter uncertainty in a way that effectively reduces uncertainty about mean performance. The optimal solution is challenging, so we use asymptotic approximations to obtain closed-form results for sampling plans. The results apply to a wide class of stochastic models, including situations where the mean performance is unknown but estimated with simulation. Analytical and empirical results for the M/M/1 queue, a quadratic response-surface model, and a simulated critical care facility illustrate the ideas.
Tags: No tags from this library for this title. Log in to add tags.
Item type Current location Call number Status Date due Barcode Item holds
INSEAD Article Europe Campus
Available BC007692
Total holds: 0

Ask Qualtrics

The design of many production and service systems is informed by stochastic model analysis. But the parameters of statistical distributions of stochastic models are rarely known with certainty, and are often estimated from field data. Even if the mean system performance is a known function of the model's parameters, there may still be uncertainty about the mean performance because the parameters are not known precisely.
Several methods have been proposed to quantify this uncertainty, but data sampling plans have not yet been provided to reduce parameter uncertainty in a way that effectively reduces uncertainty about mean performance. The optimal solution is challenging, so we use asymptotic approximations to obtain closed-form results for sampling plans. The results apply to a wide class of stochastic models, including situations where the mean performance is unknown but estimated with simulation. Analytical and empirical results for the M/M/1 queue, a quadratic response-surface model, and a simulated critical care facility illustrate the ideas.

Digitized

There are no comments for this item.

Log in to your account to post a comment.
Koha 18.11 - INSEAD Catalogue
Home | Contact Us | What's Koha?