An Algebraic Approach to Formulating and Solving Large Models for Sequential Decisions Under Uncertainty

Craig W. Kirkwood
Additional contact information
Craig W. Kirkwood: Department of Decision and Information Systems, Arizona State University, Tempe, Arizona 85287-4206

Management Science, 1993, vol. 39, issue 7, 900-913

Abstract: This article presents an algebraic approach to formulating and solving large models for sequential decisions under uncertainty. With this approach, decision analysis optimization methods can be applied to complex decision problems which are generally analyzed in management science practice using heuristics. Using the approach, a decision problem is formulated in terms of decision variables, random variables, and functions relating these variables. This leads to a compact representation, and a simple algorithm can be used to quickly solve algebraic models that would have decision trees with several hundred thousand endpoints. An application to research and development planning illustrates the usefulness of such large sequential decision models.

Keywords: decision analysis; decision trees; model formulation; stochastic models; research and development planning (search for similar items in EconPapers)
Date: 1993
References: Add references at CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.39.7.900 (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:ormnsc:v:39:y:1993:i:7:p:900-913

Access Statistics for this article

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