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LISS2019: Proceedings of the 9th International Conference on Logistics, Informatics and Service Sciences 2020 ed. [Kietas viršelis]

Edited by , Edited by , Edited by , Edited by , Edited by
  • Formatas: Hardback, 987 pages, aukštis x plotis: 235x155 mm, weight: 1664 g, 210 Illustrations, color; 113 Illustrations, black and white; XVI, 987 p. 323 illus., 210 illus. in color., 1 Hardback
  • Išleidimo metai: 10-Jul-2020
  • Leidėjas: Springer Verlag, Singapore
  • ISBN-10: 9811556814
  • ISBN-13: 9789811556814
Kitos knygos pagal šią temą:
  • Formatas: Hardback, 987 pages, aukštis x plotis: 235x155 mm, weight: 1664 g, 210 Illustrations, color; 113 Illustrations, black and white; XVI, 987 p. 323 illus., 210 illus. in color., 1 Hardback
  • Išleidimo metai: 10-Jul-2020
  • Leidėjas: Springer Verlag, Singapore
  • ISBN-10: 9811556814
  • ISBN-13: 9789811556814
Kitos knygos pagal šią temą:

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.

The Risk Sharing of Inter-city Railway PPP Project in China.- The
Integration Degree of Logistics Industry and Regional Agglomeration
Industry.- Governments 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.