Integration of Information and Optimization Models for Routing in City Logistics

1. Front Matter

   Pages i-xiv

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2. Introduction

   • Jan Fabian Ehmke

   Pages 1-5

3. Problem Description

   1. Front Matter

      Pages 7-7

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4. Problem description

   1. City Logistics

      • Jan Fabian Ehmke

      Pages 9-22

   2. Attended Home Delivery

      • Jan Fabian Ehmke

      Pages 23-33

5. Information Models

   1. Front Matter

      Pages 35-35

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6. Information models

   1. Knowledge Discovery and Data Mining

      • Jan Fabian Ehmke

      Pages 37-57

   2. Analysis of Floating Car Data

      • Jan Fabian Ehmke

      Pages 59-80

7. Integration of Information Models

   1. Front Matter

      Pages 81-81

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8. Integration of information models

   1. Provision of Distance Matrices

      • Jan Fabian Ehmke

      Pages 83-104

   2. Evaluation of Information Models

      • Jan Fabian Ehmke

      Pages 105-116

9. Optimization Models

   1. Front Matter

      Pages 117-117

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10. Optimization models

    1. Routing in City Logistics

       • Jan Fabian Ehmke

       Pages 119-156

    2. Evaluation of Optimization Models

       • Jan Fabian Ehmke

       Pages 157-177

    3. Conclusions and Outlook

       • Jan Fabian Ehmke

       Pages 179-182

11. Back Matter

    Pages 183-197

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​As urban congestion continues to be an ever increasing problem, routing in these settings has become an important area of operations research. This monograph provides cutting-edge research, utilizing the recent advances in technology, to quantify the value of dynamic, time-dependent information for advanced vehicle routing in city logistics. The methodology of traffic data collection is enhanced by GPS based data collection, resulting in a comprehensive number of travel time records. Data Mining is also applied to derive dynamic information models as required by time-dependent optimization. Finally, well-known approaches of vehicle routing are adapted in order to handle dynamic information models.

This book interweaves the usually distinct areas of traffic data collection, information retrieval and time-dependent optimization by an integrated methodological approach, which refers to synergies of Data Mining and Operations Research techniques by example of city logistics applications. These procedures will help improve the reliability of logistics services in congested urban areas.​

• Data Mining
• Information Retrieval
• Logistics
• Time Dependent Optimization
• Traffic Data Collection
• Urban Planning

• , Carl-Friedrich Gauss Dept of Economics, Technical University of Braunschweig, Braunschweig, Germany

  Jan Fabian Ehmke

Jan Fabian Ehmke has been committed to optimization of urban transportation since his days as a high school student. He majored in Decision Support, Information Systems, and Urban Traffic Management at Technical University of Braunschweig. Besides his university obligations, he worked in consulting engineer’s office, and in 2007 he joined Dr. Dirk Christian Mattfeld’s group as a research assistant. Since then he has been involved in a variety of traffic and transportation projects ranging from industry collaborations to purely academic contexts. Having a background in management science, he has focused on interweaving the commonly distinct areas of information systems, operations research, and traffic engineering. Ehmke has continuously published and presented his work, receiving particular attention from the aforementioned scientific communities. His recently finished PhD thesis involves a comprehensive treatment of the distinct areas’ contributions to dynamic routing of city logistics services. He is currently preparing for a PostDoc visit at the University of Iowa.

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