Average cost per unit time control of stochastic manufacturing systems: Revisited

T. E. Duncan, B. Pasik-Duncan and Ł. Stettner

Mathematical Methods of Operations Research, 2001, vol. 54, issue 2, 259-278

Abstract: An optimal production planning for a stochastic manufacturing system is considered. The system consists of a single, failure-prone machine that produces a finite number of different products. The objective is to determine a rate of production that minimizes an average cost per unit time criterion where the demand is random. The results given in this paper are based on some large deviation estimates and the Hamilton-Jacobi-Bellman equations for convex functions. Copyright Springer-Verlag Berlin Heidelberg 2001

Keywords: Key words: dynamic programming; stochastic manufacturing systems; large deviations; average cost per unit time (search for similar items in EconPapers)
Date: 2001
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1007/s001860100146 (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:spr:mathme:v:54:y:2001:i:2:p:259-278

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/00186

DOI: 10.1007/s001860100146

Access Statistics for this article

Mathematical Methods of Operations Research is currently edited by Oliver Stein

More articles in Mathematical Methods of Operations Research from Springer, Gesellschaft für Operations Research (GOR), Nederlands Genootschap voor Besliskunde (NGB)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().