Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals


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.

Marvin Kastner

Prozessanalysen und -prognosen in der Supply Chain, Maschinelles Lernen in der Logistik

Carlos Jahn

Einsatz maschinellen Lernens im maritimen Umfeld, Big Data Analysen in der Seeschifffahrt, Reinforcement Learning für autonome Navigation, Computer Vision in Hafen und Logistik