IMPROVABILITY OF THE FABRICATION LINE IN A SHIPYARD

The ship production process is a complex manufacturing system involving numerous working stations mutually interconnected by transport devices and buffers. Such a production system can be efficiently modeled using the stochastic system approach and Markov chains. Once formulated, the mathematical model enables analysis of the governing production system properties like the production rate, work-in-process, and probabilities of machine blockage and starvation that govern the production system bottleneck identification and its continuous improvement. Although the continuous improvement of the production system is a well-known issue, it is usually based on managerial intuition or more complex discrete event simulation yielding sub-optimal results. Therefore, a semi-analytical procedure for the improvability analysis using the Markov chain framework is presented in this paper in the case of the shipyard’s fabrication lines. Potential benefits for the shipyards are pointed out as the main gain of the improvability analysis

Lernfähiges Assistenzsystem zur Optimierung der Planung maritimer Großprojekte in der Anbahnungsphase

In der vorliegenden Dissertation wird ein digitales Assistenzsystem entwickelt, das die Planungsprozesse in der Anbahnungsphase maritimer Großprojekte unterstützt und optimiert. Dafür wird ein branchenspezifischer Simulationskern entwickelt, dessen Daten-basis mittels Machine Learning vervollständigt wird, um eine Anwendung in frühen Projektphasen trotz der vergleichsweise schlechten Datenlage überhaupt erst zu ermöglichen. Zudem wird ein Data Interface entwickelt, um die Integration der Teilsysteme hin zu einem gesamtheitlichen Assistenzsystem zu gewährleisten

ABSTRACT STOCHASTIC SHIPYARD SIMULATION WITH SIMYARD

SimYard is a stochastic shipyard simulation tool designed to evaluate the labor costs of executing different schedules in a shipyard production environment. SimYard simulates common production problems such as task delays and labor shortages. A simulated floor manager reacts to problems as they arise. Repeatedly simulating multiple schedules allows the user to compare the schedules on many different metrics, such as expected labor costs and the probability of missing the deadline. A SimYard simulation is driven by many inputs that describe the shipyard being simulated. Determining the correct values for these inputs can be framed as a multivariate calibration problem, which can be solved using inverse regression methods. Predictive sampling from the resulting model provides an appropriate adjustment for statistical uncertainty.