Abstract
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.
Highlights
Seaports are the interface between various transport modes in the maritime supply chain
This study provides an approach to solving integrated decision problems at container terminals
E.g., a full-factorial design with a coarse grid—leads to either very large simulation studies or a selection of experiments biased by the researcher’s beliefs. These shortcomings of optimization alone and manually designed large simulation studies are partly overcome by the presented simulation-based optimization approach
Summary
Seaports are the interface between various transport modes in the maritime supply chain. Another example is the relationship between gate organization and dispatching in the yard: If the number of truck arrivals is regulated by a truck appointment system, this influences the number of handling orders for RTGs and affects dispatching [7] These are just two examples of the numerous interactions between decision problems. The operations on the waterside are focused on due to the high costs related to the berth time of a ship This means that the number of QCs, YTs, and RTGs as well as the integration of the handling processes need to be jointly considered to ensure efficient operations. This results in a very complex problem for which a solution is difficult to find and, especially under the real-time requirements of a container terminal, is almost impossible to solve in terms of computing power It is worthwhile stepping back and considering different methods, including the use of policies
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