Frank Wiedra, M.Sc. (Chair for Business Administration, HSU)
In Roll-on/Roll-off (RoRo) terminals, maritime transshipment of vehicles is carried out as pre- and post-processing for maritime transportation with RoRo ships. In the context of this case study, we focus on personnel scheduling in RoRo terminals, in particular scheduling of mono- and multi-skilled personnel who drive vehicles from parking areas of a RoRo terminal onto decks of a RoRo ship, lash vehicles for security during maritime transportation and drive shuttles for moving personnel between decks and parking areas. Groups of vehicles, which are stored in these parking areas and which are planned to be loaded, are called batches. Because unloadings mirror loadings in reverse order, we focus on loadings for simplicity. Motivated by sudden incidents concerning the personnel, such as mass COVID-19 disease or strikes, we consider the specific case that internal personnel are insufficient for loading all batches as planned, even with expansion by external personnel from temporary employment agencies. For quick response decision making, we use weightings to decide whether batches will be loaded or not. We formulate a Constraint Programming (CP) model with the hierarchical objectives to maximize the sum of weightings of loaded batches and to minimize the number of used external personnel. We solve generated datasets of RoRo terminals with the CP Optimizer and propose a model-based matheuristic, which is inspired by a rule-based heuristic mirroring the human planner’s manual personnel scheduling.
Together with the project group hpc.bw we want to use the supercomputer HSUper to find optimal solutions for the generated datasets. So far, the exact algorithm of the CP Optimizer in combination with commercial hardware could not provide such solutions in acceptable runtimes. Furthermore, we want to analyze the optimal solutions and compare them with those of the proposed model-based matheuristic.