A decision support system for improved resource planning and truck routing at logistic nodes

Alessandro Hill () and Jürgen W. Böse ()
Additional contact information
Alessandro Hill: Hamburg University of Technology
Jürgen W. Böse: Hamburg University of Technology

Information Technology and Management, 2017, vol. 18, issue 3, No 5, 251 pages

Abstract: Abstract In this paper, we present an innovative decision support system that simultaneously provides predictive analytics to logistic nodes as well as to collaborating truck companies. Logistic nodes, such as container terminals, container depots or container loading facilities, face heavy workloads through a large number of truck arrivals during peak times. At the same time, truck companies suffer from augmented waiting times. The proposed system provides forecasted truck arrival rates to the nodes and predicted truck gate waiting times at the nodes to the truck companies based on historical data, economic and environmental impact factors. Based on the expected workloads, the node personnel and machinery can be planned more efficiently. Truck companies can adjust their route planning in order to minimize waiting times. Consequently, both sides benefit from reduced truck waiting times while reducing traffic congestion and air pollution. We suggest a flexible cloud based service that incorporates an advanced forecasting engine based on artificial intelligence capable of providing individual predictions for users on all planning levels. In a case study we report forecasting results obtained for the truck waiting times at an empty container depot using artificial neural networks.

Keywords: Decision support systems; Forecasting; Predictive analytics; Truck routing; Resource planning (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10799-016-0267-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infotm:v:18:y:2017:i:3:d:10.1007_s10799-016-0267-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10799

DOI: 10.1007/s10799-016-0267-3

Access Statistics for this article

Information Technology and Management is currently edited by Raymond Patterson and Erik Rolland

More articles in Information Technology and Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().