Options
A Robust Berth Allocation Optimization Procedure Based on Machine Learning
Publikationstyp
Book Part
Date Issued
2021-08-31
Sprache
English
Author(s)
First published in
Start Page
107
End Page
122
Citation
Lecture Notes in Logistics: 107-122 (2021)
Publisher DOI
Scopus ID
Publisher
Springer
ISBN
978-3-030-85842-1
978-3-030-85843-8
In berth allocation planning, container vessels are to be assigned to berthing locations and times at the quay of a container terminal. Terminal operators often aim to provide the best possible service quality to the shipping companies, i.e. especially short waiting times. However, the actual arrival times of vessels are uncertain due to external influences, e.g. wind and current or technical defects, which impedes the planning and may lead to conflicts with respect to scheduled berths. In this work, Machine Learning techniques are applied to enable the determination of patterns in AIS data and hence to develop forecasts of the arrival times. Moreover, with a robust optimization approach based on Dynamic Time Buffers, uncertainty is proactively considered in the planning phase, resulting in a robust berthing schedule. The results of this new approach are evaluated from an ex post perspective using real ship data and actual ship arrival times. It is shown by a numerical study that the average number of conflicts can be reduced significantly by this approach and that the new concept improves the schedules’ robustness.
Subjects
MLE@TUHH
DDC Class
600: Technology