Each time a sequence of interdependent decisions occur, making a single decision raises the need to anticipate the future impact on the overall decision-making process. Anticipatory support needed for a variety of dynamic and stochastic decision problems of different operational contexts such as finance, manufacturing energy management, and transportation. As a result of stochastic and dynamic decision problems completing a series of optimization problems to be formulated and solved with the anticipation of the decision making process is left. However, actually solve the problem of dynamic decision by approximate dynamic programming is still a major scientific challenge. Therefore, the industry demand for dynamic scheduling and routing is still dominated satisfied with a purely heuristic approach to make anticipatory decisions. While this may work well for some of the dynamic decision problem, this approach is not transferable findings to other, related problems. This book has served two main purposes: ? Fully integrate the Markov decision processes, dynamic programming, data mining and optimization and introduces a new perspective on dynamic programming approximations. This demonstrates for the first time how to successfully solve a dynamic vehicle routing problem with dynamic programming approximations.