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E-raamat: Integration of Information and Optimization Models for Routing in City Logistics

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Integration of Information and Optimization Models for Routing in City LogisticsSuurem pilt
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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.?

Urban congestion is a growing and seemingly intractable problem. This award-winning paper interweaves traffic data collection with data mining and operations research techniques to quantify the value of dynamic and time-sensitive information in city logistics.
1 Introduction
1(8)
Part I Problem Description
2 City Logistics
9(14)
2.1 Challenges
9(3)
2.1.1 Evolution of Supply Chains
10(2)
2.1.2 Increasing (Freight) Traffic
12(1)
2.2 Solution Concepts
12(5)
2.2.1 Perspective of Different Stakeholders
14(1)
2.2.2 Urban Consolidation Centers
15(1)
2.2.3 City Logistics Initiatives
16(1)
2.3 Modeling
17(2)
2.4 Planning Systems
19(4)
2.4.1 Levels of Planning
19(1)
2.4.2 Architecture of a Planning System
20(3)
3 Attended Home Delivery
23(14)
3.1 Online Retail
23(3)
3.2 Types of Last-Mile Delivery
26(3)
3.3 Customer Time Windows
29(2)
3.3.1 Tactical Planning
29(1)
3.3.2 Operational Planning
30(1)
3.4 Implications
31(6)
Part II Information Models
4 Knowledge Discovery and Data Mining
37(22)
4.1 Knowledge Discovery Process
37(7)
4.1.1 Preprocessing
39(2)
4.1.2 Data Mining
41(2)
4.1.3 Verification
43(1)
4.2 Cluster Analysis
44(11)
4.2.1 Clustering Approaches
46(2)
4.2.2 Clustering Algorithms
48(4)
4.2.3 Validation of Clusterings
52(3)
4.3 Exploratory Data Analysis
55(4)
5 Analysis of Floating Car Data
59(24)
5.1 Data Collection
61(5)
5.1.1 Traditional Approach
61(1)
5.1.2 Telematics-Based Approach
62(4)
5.2 Preprocessing
66(3)
5.2.1 Attribute "Time"
66(1)
5.2.2 Attribute "Link"
66(1)
5.2.3 Attribute "Speed"
67(1)
5.2.4 Temporal Distribution of Measurements
68(1)
5.2.5 Spatial Distribution of Measurements
68(1)
5.3 First Level Aggregation
69(3)
5.4 Second Level Aggregation
72(3)
5.4.1 Preprocessing
72(1)
5.4.2 Clustering Tendency
72(1)
5.4.3 Clustering Approach
73(1)
5.4.4 Number of Clusters
74(1)
5.5 Exploratory Data Analysis
75(8)
5.5.1 First Level Aggregation
76(2)
5.5.2 Second Level Aggregation
78(5)
Part III Integration of Information Models
6 Provision of Distance Matrices
83(22)
6.1 Static Information Models
86(4)
6.1.1 Digital Roadmap
86(3)
6.1.2 Implementation
89(1)
6.2 Time-Dependent Information Models
90(10)
6.2.1 Modeling of Time Dependence
91(6)
6.2.2 Implementation
97(3)
6.3 Computation of Shortest Paths
100(5)
6.3.1 Shortest Path Problem
100(2)
6.3.2 Time-Dependent Shortest Path Problem
102(3)
7 Evaluation of Information Models
105(14)
7.1 Experimental Setup
105(2)
7.1.1 Traveler Scenarios
106(1)
7.1.2 Traffic Scenarios
106(1)
7.1.3 Information Models
107(1)
7.2 Simulation and Evaluation of Shortest Paths
107(2)
7.3 Computational Results
109(10)
7.3.1 Evaluation of Example Itineraries
109(3)
7.3.2 Overall Evaluation
112(7)
Part IV Optimization Models
8 Routing in City Logistics
119(38)
8.1 Routing of a Single Vehicle
120(14)
8.1.1 Traveling Salesman Problem
120(7)
8.1.2 Time-Dependent Traveling Salesman Problem
127(7)
8.2 Routing of a Fleet of Vehicles
134(6)
8.2.1 Vehicle Routing Problem
134(3)
8.2.2 Time-Dependent Vehicle Routing Problem
137(3)
8.3 Customer Time Windows
140(17)
8.3.1 Vehicle Routing Problem with Time Windows
141(4)
8.3.2 Time-Dependent Vehicle Routing Problem with Time Windows
145(12)
9 Evaluation of Optimization Models
157(22)
9.1 Experimental Setup
157(4)
9.1.1 Customer Scenarios
158(1)
9.1.2 Information Models
158(1)
9.1.3 Evaluation of Heuristics
159(2)
9.2 Routing of a Single Vehicle
161(7)
9.2.1 Customer Scenario 1
162(2)
9.2.2 Customer Scenario 2
164(1)
9.2.3 Customer Scenario 3
165(3)
9.3 Routing of a Fleet of Vehicles
168(4)
9.3.1 Performance of Neighborhood Operators
168(2)
9.3.2 Computational Results
170(2)
9.4 Customer Time Windows
172(7)
9.4.1 Role of Customer Time Windows
173(1)
9.4.2 Simulation of Customer Time Windows
173(1)
9.4.3 Computational Results
174(5)
10 Conclusions and Outlook
179(4)
References 183(12)
Index 195
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 engineers office, and in 2007 he joined Dr. Dirk Christian Mattfelds 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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