The advantage of small machines in a stochastic fluid production process
Nicole Bäuerle ()
Mathematical Methods of Operations Research, 1998, vol. 47, issue 1, 83-97
Abstract:
We consider a stochastic fluid production model, where m machines which are subject to breakdown and repair, produce a fluid at ratep > 0 per machine if it is working. This fluid is fed into an infinite buffer with stochastic output rate. Under the assumption that the machine processes are independent and identically distributed, we prove that the buffer content at timet is less or equal in the increasing convex ordering to the buffer content at time t of a model withm′ ≤m machines and production ratep′ =m/m′ p. This formulation includes a conjecture posed by Mitra [6]. More-over, it is shown how to extend this result to Brownian flow systems, systems obtained by diffusion approximation and simple stochastic flow networks like tandem buffer and assembly systems. Copyright Physica-Verlag 1998
Keywords: Stochastic fluid models; Increasing convex ordering; Brownian flow systems; Tandem buffer systems; Assembly systems (search for similar items in EconPapers)
Date: 1998
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1007/BF01193838 (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:47:y:1998:i:1:p:83-97
Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/00186
DOI: 10.1007/BF01193838
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 ().