Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Sign In to gain access to subscriptions and/or personal tools.
SIMULATION
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Kaplan, A.
Right arrow Articles by Orr, D. A.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Data enrichment for simulation

Alan Kaplan

AMC Inventory Research Office U. S. Army Logistics Management Center Frankford Arsenal Philadelphia, Pennsylvania 19137

Donald A. Orr

AMC Inventory Research Office U. S. Army Logistics Management Center Frankford Arsenal Philadelphia, Pennsylvania 19137

Data enrichment in the present context is concerned with the creation of a stochastic time series whose parameters are consistent with a prior data base and whose cumulative values over a given period satisfy constraints dictated by the prior data base. The specific problem is to generate statistical data for shorter time intervals than the raw data provides. The need for this type of enrichment arises, for example, when historical data gives total demand in each quarter over a multiyear period but the output of a simulation is sensitive to the manner in which demand is distributed over each quarter.

The method utilizes a random fraction to select re cursively a portion of the remaining demand in a quarter. Formulae for the mean and variance of the random fraction are derived. This random fraction can be computer generated with a single random number using hybrid spike-uniform distributions with given means and variances. The properties of such dis tributions are derived, and may have applications in other contexts.

SIMULATION, Vol. 26, No. 6, 177-183 (1976)
DOI: 10.1177/003754977602600603
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?