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Item type:Research Data, Access status: without files , ConFlowGen(2025-08-15); ; A generator for synthetic container flows at maritime container terminals with a focus on yard operations.Data Type: Software12 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Robust Berth Scheduling Using Machine Learning for Vessel Arrival Time Prediction - refined dataset(2022-07-22); ; ; ; The refined dataset consists of edited data from raw AIS data. The data is organised as follows: There is one csv file per cargo ship (271 files in total from 1.csv to 271.csv). This file contains all calls of the respective ship at the Port of Miami for the given period from 2018 to 2020. To anonymize the data, the identifying ship data, i.e. the MMSI, was removed. Further information can be found in the paper in Section 3.1. (paper is under review and will be linked here as soon as available) The raw AIS data which are used are available on the websites of the National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management. An overview is published here: https://marinecadastre.gov/ais/Data Type: Dataset944 1746 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: without files , Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals - Dataset(2021-01-27); ; ; 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. The dataset is stored in the Excel file named Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals - Dataset.xlsx.This contains the data to create the figures and the data to run the statistics. The data analysis is stored in the Jupyter Notebook named Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals - Data Analysis.ipynb. This contains tje code to create the figures and the data to run the statistics.Data Type: Dataset12