LISS2019
Proceedings of the 9th International Conference on Logistics, Informatics and Service Sciences
Published on 10. July 2021
Book
Paperback/Softback
XVI, 987 pages
978-981-15-5684-5 (ISBN)
Description
This book focuses on AI and data-driven technical and management innovations in logistics, informatics and services. The respective papers analyze in detail the latest fundamental advances in the state of the art and practice of logistics, informatics, service operations and service science. The book gathers the outcomes of the "9th International Conference on Logistics, Informatics and Service Sciences," which was held at the University of Maryland, USA.
More details
Edition
2020 ed.
Language
English
Place of publication
Singapore
Singapore
Target group
Professional and scholarly
Illustrations
113 s/w Abbildungen, 210 farbige Abbildungen
XVI, 987 p. 323 illus., 210 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 54 mm
Weight
1486 gr
ISBN-13
978-981-15-5684-5 (9789811556845)
DOI
10.1007/978-981-15-5682-1
Schweitzer Classification
Other editions
Additional editions
Juliang Zhang | Martin Dresner | Runtong Zhang
LISS2019
Proceedings of the 9th International Conference on Logistics, Informatics and Service Sciences
Book
07/2020
Springer
€192.59
Shipment within 3-4 weeks
Content
The Risk Sharing of Inter-city Railway PPP Project in China.- The Integration Degree of Logistics Industry and Regional Agglomeration Industry.- Government's Role Choice in Market Regulation from an Information-Disclosure Perspective.- A Data-driven Methodology for Operational Risk Analytics Using Bayesian Network.- Order Batching of Intelligent Warehouse Order Picking System Based on Logistics Robots.- Modeling and Analysis on the Capacity of Centralized Directional Multi-hop Ad Hoc Networks.- Adaptive Routing Based on Wireless Ad Hoc Networks.- Grid Path Planning for Mobile Robots with Improved Q-learning Algorithm.- Content-based Weibo User Interest Recognition.- Intellectual Structure Detection Using Heterogeneous Data Clustering Method.- Technical Efficiency Analysis of Bus Companies.