Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: http://hdl.handle.net/10419/228951
Erscheinungsjahr: 
2020
Quellenangabe: 
[Editor:] Jahn, Carlos [Editor:] Kersten, Wolfgang [Editor:] Ringle, Christian M. [Title:] Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 30 [ISBN:] 978-3-7531-2347-9 [Publisher:] epubli GmbH [Place:] Berlin [Year:] 2020 [Pages:] 211-246
Verlag: 
epubli GmbH, Berlin
Zusammenfassung: 
Purpose: Truck appointment systems (TAS) are a widely used method to alleviate peaks in truck arrivals at container terminals in seaports and in the hinterland. One big advantage is the opportunity to reduce operation costs for the terminals and the truck queue length in front of the terminal gate. This study aims to analyze and classify different approaches used in science and industry to determine the quota of allowed trucks per time window. Methodology: A comprehensive systematic literature analysis is applied to identify the different approaches to determine the quota of time windows in science and in industry. Findings: The results of the study show that many approaches have been based on experience and are mostly used to improve individual terminals rather than the port as a whole. Methods used to improve and analyze interrelationships are mainly methods of mathematical optimization and simulation. Originality: The question under consideration was mostly only marginally considered in existing investigations, even though it has a major impact on the success of a TAS. Furthermore, only individual solutions have been examined so far and not the suitability of the approaches compared.
Schlagwörter: 
Logistics
Industry 4.0
Supply Chain Management
Sustainability
City Logistics
Maritime Logistics
Data Science
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by-sa Logo
Dokumentart: 
Conference Paper

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.