الفهرس 
CHAPTER 1: Introduction and Basic ConceptsOnly 14 pages are availabe for public view
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Abstract
Stochastic programming methods are an important for the investigation of
purposeful processes in production planning, engineering, agriculture,
telecommunications, transportation models, military purposes and
technology. Linear programming under uncertainty or, as it sometimes
termed, Stochastic programming with recourse, was suggested by (Beale
and Dantzig). Charnes and Cooper have presented a trial approach called
chance-constrained programming in which each constraint must be
satisfied with a certain tolerance probability. In this thesis a solution for
stochastic nonlinear programming problem is presented by developing a
new theorem. This solution required converting the stochastic nonlinear
programming problem to a deterministic nonlinear programming problem,
which can be solved by the separable programming method.
The thesis comprises five chapters, these are organized as follows:
Chapter one: Introduction and Basic Concepts
In this chapter we presented a survey concerning some
definitions and fundamental results of probability theory
required for stochastic processes, the solution of nonlinear
programming methods, and stochastic programming
methods. In addition, the formulation of stochastic nonlinear
programming problem.
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Chapter two: Nonlinear Programming Methods
In this chapter the nonlinear programming methods is
introduced. This consists of unconstrained nonlinear
programming methods and constrained nonlinear
programming methods.
Chapter three: Stochastic Processes
In this chapter we presented the stochastic processes, its
types, classifications, practical applications, and some
examples. Also the stochastic programming and the
stochastic linear programming are presented.
Chapter four: Stochastic Nonlinear Programming Problem Solution
A solution for stochastic nonlinear programming problem is
proposed. This solution required converting the stochastic
nonlinear programming problem to a deterministic nonlinear
programming problem through developing the theorem
which solves stochastic linear programming problem to a
new one to solve the stochastic nonlinear programming
problem (theorem 4.3.1), which can be solved by the
separable programming method. We proved that theorem
and verified it by solving two examples by the stochastic
nonlinear programming problem (examples 4.1 and 4.2).
Chapter five: conclusion and future work
In this chapter provides the conclusions arrived in carrying
out this thesis and makes recommendations in the field of
stochastic nonlinear programming problems.