| Vorliegende Sprache |
eng |
| Hinweise auf parallele Ausgaben |
383418305 Buchausg. u.d.T.: ‡Ehmke, Jan Fabian, 1980 - : Integration of information and optimization models for routing in city logistics |
| ISBN |
978-1-4614-3627-0 |
| Name |
Ehmke, Jan Fabian |
| T I T E L |
Integration of Information and Optimization Models for Routing in City Logistics |
| Verlagsort |
Boston, MA |
| Verlag |
Springer US |
| Erscheinungsjahr |
2012 |
| 2012 |
| Umfang |
Online-Ressource (XIV, 197p. 76 illus., 17 illus. in color, digital) |
| Reihe |
International Series in Operations Research & Management Science ; 177 |
| Notiz / Fußnoten |
Description based upon print version of record |
| Weiterer Inhalt |
Integration of Information and Optimization Models for Routing in City Logistics; Foreword; Contents; Abbreviations; Symbols; 1 Introduction; Part I Problem Description; 2 City Logistics; 2.1…Challenges; 2.1.1 Evolution of Supply ChainsSupply Chain'' \b; 2.1.2 Increasing (Freight) Traffic; 2.2…Solution Concepts; 2.2.1 Perspective of Different Stakeholders; 2.2.2 Urban Consolidation CentersUrban consolidaten center, UCC'' \b; 2.2.3 City LogisticsCity logistics Initiatives; 2.3…Modeling; 2.4…Planning SystemsPlanning system'' \b; 2.4.1 Levels of PlanningPlanning. 2.4.2 Architecture of a Planning SystemPlanning system3 Attended Home Delivery; 3.1…Online Retail; 3.2…Types of Last-Mile Delivery; 3.3…Customer Time Windows; 3.3.1 Tactical Planning; 3.3.2 Operational Planning; 3.4…Implications; Part II Information Models; 4 Knowledge Discovery and Data Mining; 4.1…Knowledge Discovery Process Knowledge Discovery Process, KDD; 4.1.1 Preprocessing; 4.1.1.1 Data CleaningPreprocessingData Cleaning'' \bData Cleaning'' \b; 4.1.1.2 Data IntegrationPreprocessingData IntegrationData Integration; 4.1.2 Data Mining Data Mining, DM'' \b; 4.1.3 Verification. 4.2…Cluster AnalysisCluster analysis'' \t ''See Clustering4.2.1 Clustering Approaches; 4.2.2 Clustering AlgorithmsClusteringCluster algorithm; 4.2.2.1 k-Means Clusteringk-Means'' \b; 4.2.2.2 k-Medoids Clusteringk-Medoids'' \b; 4.2.2.3 DBSCANClusteringDBSCAN'' \b; 4.2.3 Validation of Clusterings; 4.2.3.1 Clustering Tendency (Hopkin's Statistics) Cluster validationClustering tendency Clustering tendency Cluster validationHopkin's statistics Hopkin's statistics'' \b; 4.2.3.2 Davies-Bouldin IndexCluster validationDavies-Bouldin index, DBI Davies-Bouldin index, DBI. 4.2.3.3 Suitable Number of Clusters (Elbow Criterion) Cluster validationNumber of clusters4.3…Exploratory Data AnalysisExploratory Data Analysis, EDA'' \bData Mining, DMExploratory Data Analysis, EDA'' \b; 5 Analysis of Floating Car Data; 5.1…Data CollectionData collection; 5.1.1 Traditional Approach; 5.1.2 TelematicsTelematics-Based Approach; 5.1.2.1 Data CollectionData collection by FCD; 5.1.2.2 Data CollectionData collection by Taxi-FCDTaxi-FCD; 5.2…Preprocessing; 5.2.1 Attribute ''Time''; 5.2.2 Attribute ''Link''Link; 5.2.3 Attribute ''Speed''; 5.2.4 Temporal Distribution of Measurements. 5.2.5 Spatial Distribution of Measurements5.3…First Level AggregationFirst level aggregation; 5.4…Second Level AggregationSecond level aggregation; 5.4.1 Preprocessing; 5.4.2 Clustering TendencyCluster validationClustering tendencyClustering tendency; 5.4.3 Clustering Approach; 5.4.4 Number of ClustersCluster validationNumber of clusters; 5.5…Exploratory Data AnalysisExploratory Data Analysis, EDAData Mining, DMExploratory Data Analysis, EDA; 5.5.1 First Level AggregationFirst level aggregation; 5.5.2 Second Level Aggregation Second level aggregation. Part III Integration of Information Models |
| Titelhinweis |
Buchausg. u.d.T.: ‡Ehmke, Jan Fabian, 1980 - : Integration of information and optimization models for routing in city logistics |
| ISBN |
ISBN 978-1-4614-3628-7 |
| Klassifikation |
KJMD |
| KJT |
| BUS049000 |
| 658.40301 |
| 658.70151 |
| HD30.23 |
| QH 463 |
| QR 800 |
| Kurzbeschreibung |
Introduction -- City Logistics -- Attended Home Delivery -- Knowledge Discovery and Data Mining -- Analysis of Floating Car Data -- Provision of Distance Matrices -- Evaluation of Information Models -- Routing in City Logistics -- Evaluation of Optimization Models -- Conclusions and Outlook |
| 2. Kurzbeschreibung |
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 |
| 1. Schlagwortkette |
Stadtverkehr |
| Verkehrsplanung |
| City-Logistik |
| Optimierung |
| ANZEIGE DER KETTE |
Stadtverkehr -- Verkehrsplanung -- City-Logistik -- Optimierung |
| SWB-Titel-Idn |
365273120 |
| Signatur |
Springer E-Book |
| Bemerkungen |
Elektronischer Volltext - Campuslizenz |
| Elektronische Adresse |
$uhttp://dx.doi.org/10.1007/978-1-4614-3628-7 |
| Internetseite / Link |
Volltext |
| Siehe auch |
Volltext |
| Siehe auch |
Cover |
