EconPapers    
Economics at your fingertips  
 

Sensitivity analysis and decomposition of unreliable production lines with blocking

Vassilis Kouikoglou ()

Annals of Operations Research, 2000, vol. 93, issue 1, 245-264

Abstract: The analysis of finite‐buffered, unreliable production lines is often based on the method of decomposition, where the original system is decomposed into a series of two‐stage subsystems that can be modeled as quasi birth‐death processes. In this paper, we present methods for computing the gradients of the equilibrium distribution vector for such processes. Then we consider a specific production line with finite buffers and machine breakdowns and develop an algorithm that incorporates gradient estimation into the framework of Gershwin's approximate decomposition. The algorithm is applied to the problem of workforce allocation to the machines of a production line to maximize throughput. It is shown that this problem is equivalent to a convex mathematical programming problem and, therefore, a globally optimal solution can be obtained. Copyright Kluwer Academic Publishers 2000

Date: 2000
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1023/A:1018975923886 (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:annopr:v:93:y:2000:i:1:p:245-264:10.1023/a:1018975923886

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1023/A:1018975923886

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:annopr:v:93:y:2000:i:1:p:245-264:10.1023/a:1018975923886