Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/228960
Authors: 
Kastner, Marvin
Kämmerling, Nicolas
Jahn, Carlos
Clausen, Uwe
Year of Publication: 
2020
Citation: 
[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 [Year:] 2020 [Pages:] 485-519
Abstract: 
Purpose: When container terminals are planned or converted, among others the most suitable container handling system needs to be selected and the appropriate terminal layout needs to be designed. These two planning activities are mutually dependent and affect the costs and future operational performance. This leads to the question of how to arrive at a (near-)optimal solution for given criteria. Methodology: A mapping review is conducted to investigate how the container handling system is selected and how the terminal layout is designed. Literature is examined regarding the employed methodology, the performance indicator(s) to optimize, and the way terminal layout and equipment selection have been jointly considered. Findings: Various methods have been used to assess a suitable container handling system and the appropriate layout. Commonly, mathematical optimization is used to arrive at a suggestion and simulation is the tool to evaluate proposed decisions. Aspects such as handling costs, travel distances, or ecological factors are sought to be optimized. Originality: Several literature reviews in the past years investigated approaches to the plethora of scheduling problems at container terminals. Here, the two strategic planning activities equipment selection and layout planning are presented in detail. This publication focuses on how the dependency of the two activities has been han-dled in literature.
Subjects: 
Logistics
Industry 4.0
Supply Chain Management
Sustainability
City Logistics
Maritime Logistics
Data Science
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-sa/4.0/
Document Type: 
Conference Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.