A State Aggregation Approach to Manufacturing Systems Having Machine States with Weak and Strong Interactions

J. Jiang and Suresh Sethi
Additional contact information
J. Jiang: University of Toronto, Toronto, Canada

Operations Research, 1991, vol. 39, issue 6, 970-978

Abstract: A hierarchical approach to control a manufacturing system, subject to multiple machine states modeled by a Markov process with weak and strong interactions, is suggested. The idea is to aggregate strongly interacting or high transition probability states within a group of states and consider only the transition between these groups for the analysis of the system in the long run. We show that such an aggregation results in a problem of reduced size, whose solution can be modified in a simple way to obtain an asymptotically optimal feedback solution to the original problem. Also, an example is solved to illustrate the results developed in the paper.

Keywords: dynamic programming; optimal control: stochastic; continuous time; probability; Markov processes: hierarchical control of Markov process driven systems; production/scheduling; hierarchical planning: manufacturing with unreliable machines (search for similar items in EconPapers)
Date: 1991
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.39.6.970 (application/pdf)

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:inm:oropre:v:39:y:1991:i:6:p:970-978

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().