Please use this identifier to cite or link to this item:
Publisher URL:
Title: Workload forecasting of a logistic node using Bayesian neural networks
Language: English
Authors: Nakilcioglu, Emin Cagatay 
Rizvanolli, Anisa 
Rendel, Olaf 
Editor: Kersten, Wolfgang  
Jahn, Carlos  
Blecker, Thorsten 
Ringle, Christian M.  
Keywords: Artificial Intelligence; Blockchain
Issue Date: Sep-2022
Publisher: epubli
Source: Hamburg International Conference of Logistics (HICL) 33: 237-264 (2022)
Abstract (english): 
Purpose: Traffic volume in empty container depots has been highly volatile due to external factors. Forecasting the expected container truck traffic along with having a dynamic module to foresee the future workload plays a critical role in improving the work efficiency. This paper studies the relevant literature and designs a forecasting model addressing the aforementioned issues.
Methodology: The paper develops a forecasting model to predict hourly work and traffic volume of container trucks in an empty container depot using a Bayesian Neural Network based model. Furthermore, the paper experiments with datasets with different characteristics to assess the model’s forecasting range for various data sources.
Findings: The real data of an empty container depot is utilized to develop a forecasting model and to later verify the capabilities of the model. The findings show the performance validity of the model and provide the groundwork to build an effective traffic and workload planning system for the empty container depot in question.
Originality: This paper proposes a Bayesian deep learning-based forecasting model for traffic and workload of an empty container depot using real-world data. This designed and implemented forecasting model offers a solution with which every actor in the container truck transportation benefits from the optimized workload.
Conference: Hamburg International Conference of Logistics (HICL) 2022 
DOI: 10.15480/882.4694
ISBN: 978-3-756541-95-9
ISSN: 2365-5070
Document Type: Chapter/Article (Proceedings)
Peer Reviewed: Yes
License: CC BY-SA 4.0 (Attribution-ShareAlike 4.0) CC BY-SA 4.0 (Attribution-ShareAlike 4.0)
Part of Series: Proceedings of the Hamburg International Conference of Logistics (HICL) 
Volume number: 33
Appears in Collections:Publications with fulltext

Files in This Item:
File Description SizeFormat
Nakilcioglu et al. (2022) - Workload Forecasting of a Logistic Node Using Bayesian Neural Networks.pdfWorkload Forecasting of a Logistic Node Using Bayesian Neural Networks933,25 kBAdobe PDFView/Open
Show full item record

Page view(s)

checked on Dec 9, 2022


checked on Dec 9, 2022

Google ScholarTM


Note about this record

Cite this record


This item is licensed under a Creative Commons License Creative Commons