Integrated simulation-based optimization of operational decisions at container terminals

Journal
Algorithms  
Volume
14
Issue
2
Article Number
42
Citation
Algorithms 14 (2): 42 (2021-02)
Publisher DOI
10.3390/a14020042
Scopus ID
2-s2.0-85100634376
Publisher
MDPI AG
Is Supplemented By
10.5281/zenodo.4473250
At container terminals, many cargo handling processes are interconnected and occur in parallel. Within short time windows, many operational decisions need to be made and should consider both time efficiency and equipment utilization. During operation, many sources of disturbance and, thus, uncertainty exist. For these reasons, perfectly coordinated processes can potentially unravel. This study analyzes simulation-based optimization, an approach that considers uncertainty by means of simulation while optimizing a given objective. The developed procedure simultaneously scales the amount of utilized equipment and adjusts the selection and tuning of operational policies. Thus, the benefits of a simulation study and an integrated optimization framework are combined in a new way. Four meta-heuristics - Tree-structured Parzen Estimator, Bayesian Optimization, Simulated Annealing, and Random Search - guide the simulation-based optimization process. Thus, this study aims to determine a favorable configuration of equipment quantity and operational policies for container terminals using a small number of experiments and, simultaneously, to empirically compare the chosen meta-heuristics including the reproducibility of the optimization runs. The results show that simulation-based optimization is suitable for identifying the amount of required equipment and well-performing policies. Among the presented scenarios, no clear ranking between meta-heuristics regarding the solution quality exists. The approximated optima suggest that pooling yard trucks and a yard block assignment that is close to the quay crane are preferable.
Subjects
Container terminal
Simulation
Simulation-based optimization
meta-heuristic
horizontal transportation
hyper-parameter optimization
MLE@TUHH
DDC Class
330: Wirtschaft
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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