This book provides a straightforward overview for every researcher
interested in stochastic dynamic vehicle routing problems (SDVRPs). The
book is written for both the applied researcher looking for suitable
solution approaches for particular problems as well as for the
theoretical researcher looking for effective and efficient methods of
stochastic dynamic optimization and approximate dynamic programming
(ADP). To this end, the book contains two parts. In the first part, the
general methodology required for modeling and approaching SDVRPs is
presented. It presents adapted and new, general anticipatory methods of
ADP tailored to the needs of dynamic vehicle routing. Since stochastic
dynamic optimization is often complex and may not always be intuitive on
first glance, the author accompanies the ADP-methodology with
illustrative examples from the field of SDVRPs.
The second part of this book then depicts the application of the theory
to a specific SDVRP. The process starts from the real-world application.
The author describes a SDVRP with stochastic customer requests often
addressed in the literature, and then shows in detail how this problem
can be modeled as a Markov decision process and presents several
anticipatory solution approaches based on ADP. In an extensive
computational study, he shows the advantages of the presented approaches
compared to conventional heuristics. To allow deep insights in the
functionality of ADP, he presents a comprehensive analysis of the ADP
approaches.