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.
|Date||01 May 1970|
|Creators||Foes, Chamberlain Lambros|
|Source Sets||Portland State University|
|Source||Dissertations and Theses|
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