The availability of today's online information systems rapidly
increases the relevance of dynamic decision making within a large
number of operational contexts. Whenever a sequence of
interdependent decisions occurs, making a single decision raises
the need for anticipation of its future impact on the entire
decision process. Anticipatory support is needed for a broad
variety of dynamic and stochastic decision problems from different
operational contexts such as finance, energy management,
manufacturing and transportation. Example problems include asset
allocation, feed-in of electricity produced by wind power as well
as scheduling and routing. All these problems entail a sequence of
decisions contributing to an overall goal and taking place in the
course of a certain period of time. Each of the decisions is
derived by solution of an optimization problem. As a consequence a
stochastic and dynamic decision problem resolves into a series of
optimization problems to be formulated and solved by anticipation
of the remaining decision process.
However, actually solving a dynamic decision problem by means of
approximate dynamic programming still is a major scientific
challenge. Most of the work done so far is devoted to problems
allowing for formulation of the underlying optimization problems as
linear programs. Problem domains like scheduling and routing, where
linear programming typically does not produce a significant benefit
for problem solving, have not been considered so far. Therefore,
the industry demand for dynamic scheduling and routing is still
predominantly satisfied by purely heuristic approaches to
anticipatory decision making. Although this may work well for
certain dynamic decision problems, these approaches lack
transferability of findings to other, related problems.
This book has serves two major purposes:
It provides a comprehensive and unique view of anticipatory
optimization for dynamic decision making. It fully integrates
Markov decision processes, dynamic programming, data mining and
optimization and introduces a new perspective on approximate
dynamic programming. Moreover, the book identifies different
degrees of anticipation, enabling an assessment of specific
approaches to dynamic decision making.
It shows for the first time how to successfully solve a dynamic
vehicle routing problem by approximate dynamic programming. It
elaborates on every building block required for this kind of
approach to dynamic vehicle routing. Thereby the book has a
pioneering character and is intended to provide a footing for the
dynamic vehicle routing community."
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