Sponsor
Portland State University. Department of Applied Science
Date of Publication
5-1-1970
Document Type
Thesis
Degree Name
Master of Science (M.S.) in Applied Science
Department
Applied Science
Language
English
Subjects
Linear programming, Stochastic processes
DOI
10.15760/etd.821
Physical Description
1 online resource (133 leaves, ill.)
Abstract
This essay investigates the concept of linear programming in general and linear stochastic programming in particular. Linear stochastic programming is described as the model where the parameters of the linear programming admit random variability. The first three chapters present through a set-geometric approach the foundations of linear programming. Chapter one describes the evolution of the concepts which resulted in the adoption of the model. Chapter two describes the constructs in n-dimensional euclidian space which constitute the mathematical basis of linear programs, and chapter three defines the linear programming model and develops the computational basis of the simplex algorithm. The second three chapters analyze the effect of the introduction of risk into the linear programming model. The different approaches of estimating and measuring risk are studied and the difficulties arising in formulating the stochastic problem and deriving the equivalent deterministic problems are treated from the theoretical and practical point of view. Multiple examples are given throughout the essay for clarification of the salient points.
Rights
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
Persistent Identifier
http://archives.pdx.edu/ds/psu/9402
Recommended Citation
Foes, Chamberlain Lambros, "A study an analysis of stochastic linear programming" (1970). Dissertations and Theses. Paper 821.
https://doi.org/10.15760/etd.821
Comments
Portland State University. Dept. of Applied Science