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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

In the introduction of this book, we motivate the increasing challenge of decision support for logistical services and give an outlook how this book approaches these challenges.

Marlin Wolf Ulmer

Dynamic Vehicle Routing

Frontmatter

Chapter 2. Rich Vehicle Routing: Environment

In this chapter, we first recall the development in the field of vehicle routing problems. We then describe the characteristics of rich vehicle routing problems (RVRPs) in Sect. 2.2 focusing on uncertainty and replanning aspects. We briefly describe logistics management (LM) in Sect. 2.3, embed (rich) vehicle routing in context of LM’s hierarchical planning in Sect. 2.4, and present an overview on emerging developments and challenges inducing RVRPs in Sect. 2.5.

Marlin Wolf Ulmer

Chapter 3. Rich Vehicle Routing: Applications

In this chapter, we present the practical fields of routing applications inducing RVRPs. We analyze the applications regarding uncertainty and requirement for planning. We focus on routing in urban environments. The main purpose of this section is to give an overview of the important entities and underlying components in RVRPs as well as the most common objectives, constraints, and main drivers of uncertainty.

Marlin Wolf Ulmer

Chapter 4. Modeling

In this chapter, we model vehicle routing problems containing uncertainty and requiring stepwise planning by a (finite) Markov decision process (MDP). We define the MDP in Sect. 4.2. The MDP contains three (sub-)models: decision states, dynamic decision making, and stochastic transitions. To model routing applications as MDP, we have to determine the three (sub-)models. In Sect. 4.1, we model replanning as dynamism. In Sect. 4.4, we model a decision state for a vehicle routing’s planning situation. We model uncertainty as stochasticity. We give a short overview on how the main drivers of uncertainty generally are modeled in the literature in Sect. 4.5. We finally give an overview on how SDVRPs are modeled as MDPs in Sect. 4.6.

Marlin Wolf Ulmer

Chapter 5. Anticipation

In this chapter, the concept and definition of anticipation is recalled and anticipation is embedded in the context of MDP and SDVRP. The main idea of anticipation is to incorporate a predictive lookahead model of future stochasticity and decision making in the solution approach. Further, we present a classification for approaches based on their degree of anticipation.

Marlin Wolf Ulmer

Chapter 6. Anticipatory Solution Approaches

For the purpose of presentation, we present approaches of different degrees of anticipation in context of SDVRPs. We utilize the findings of this chapter to allow a succinct literature classification later on. We describe approaches providing non-reactive anticipation. We then focus on approaches providing reactive anticipation, in particular, methods of approximate dynamic programming.

Marlin Wolf Ulmer

Chapter 7. Literature Classification

In this chapter, we classify the relevant literature on SDVRPs regarding uncertainty, modeling, and the applied (anticipatory) solution approaches.

Marlin Wolf Ulmer

Stochastic Customer Requests

Frontmatter

Chapter 8. Motivation

In the second part of the book, we describe the process of modeling and the derivation of anticipatory solution approaches for a specific SDVRP. The main reason for replanning is given by uncertain customer requests. Therefore, we consider a dynamic vehicle routing problem with stochastic customer requests. After a short motivation, we show how the problem is modeled, present a variety of solution methods with different degree of anticipation, and compare these methods in an extensive computational study.

Marlin Wolf Ulmer

Chapter 9. SDVRP with Stochastic Requests

In the following, we rigorously model the presented routing application. To describe dynamic decision making, we use a Markov decision process. We then give an overview on the regarding literature. We focus on the approaches’ spatial and temporal anticipation.

Marlin Wolf Ulmer

Chapter 10. Solution Algorithms

In this chapter, we define the approaches for the SDVRP. We present an approach for (nearly) every class of anticipation. We treat the non-reactive approaches as benchmarks and focus on the reactive approaches. We show the required steps to achieve reactive anticipation for the given problem and depict the advantages and disadvantages of offline and online anticipation.

Marlin Wolf Ulmer

Chapter 11. Computational Evaluation

In this chapter, we evaluate and analyze the approaches. We first define the set of test instances in Sect. 11.1 and tune the algorithm parameters in Sect. 11.2. In Sect. 11.3, we compare the approaches providing non-reactive anticipation with the offline reactive anticipatory ATB. We use the findings to analyze ATB in detail in Sect. 11.4 and to compare ATB with the online reactive anticipatory approaches in Sect. 11.5.

Marlin Wolf Ulmer

Chapter 12. Conclusion and Outlook

In the final chapter of this book, we provide a brief summary, depict the main managerial implications of this book, and give an outlook on potentially fruitful areas of future research.

Marlin Wolf Ulmer

Backmatter

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