Mixture distributions for modelling demand during lead time

B R Cobb
Additional contact information
B R Cobb: Virginia Military Institute, Lexington, VA, USA

Journal of the Operational Research Society, 2013, vol. 64, issue 2, 217-228

Abstract: A mixture distribution approach to modelling demand during lead time in a continuous-review inventory model is described. Using this approach, both lead time and demand per unit time can follow state-dependent distributions. By using mixtures of truncated exponentials functions to approximate these distributions, mixture distributions that can be easily manipulated in closed form can be constructed as the marginal distributions for lead time and demand per unit time. These are then used to approximate the mixture of compound distributions for demand during lead time. The technique is illustrated by first applying it to a ‘normal-gamma’ inventory problem, then by modelling a problem with empirical distributions for lead time and demand per unit time.

Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.palgrave-journals.com/jors/journal/v64/n2/pdf/jors201239a.pdf Link to full text PDF (application/pdf)
http://www.palgrave-journals.com/jors/journal/v64/n2/full/jors201239a.html Link to full text HTML (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jorsoc:v:64:y:2013:i:2:p:217-228

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook

More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().