Stochastic models are everywhere. In manufacturing, queuing models
are used for modeling production processes, realistic inventory
models are stochastic in nature. Stochastic models are considered
in transportation and communication. Marketing models use
stochastic descriptions of the demands and buyer's behaviors. In
finance, market prices and exchange rates are assumed to be certain
stochastic processes, and insurance claims appear at random times
with random amounts. To each decision problem, a cost function is
associated. Costs may be direct or indirect, like loss of time,
quality deterioration, loss in production or dissatisfaction of
customers. In decision making under uncertainty, the goal is to
minimize the expected costs. However, in practically all realistic
models, the calculation of the expected costs is impossible due to
the model complexity. Simulation is the only practicable way of
getting insight into such models. Thus, the problem of optimal
decisions can be seen as getting simulation and optimization
effectively combined. The field is quite new and yet the number of
publications is enormous. This book does not even try to touch all
work done in this area. Instead, many concepts are presented and
treated with mathematical rigor and necessary conditions for the
correctness of various approaches are stated. Optimization of
Stochastic Models: The Interface Between Simulation and
Optimization is suitable as a text for a graduate level course on
Stochastic Models or as a secondary text for a graduate level
course in Operations Research.
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