23-10-2025 - Lecture Hall E

PhD Presentation

Sea State Estimation from Ship Motions using an Adaptive Kalman Filter with the Inclusion of Varying Forward Speed

Ryane Bourkaib

Knowing the sea state in real time is important for safe and efficient ship operations.
During this M&TT Colloquia presentation, I will present an Adaptive Kalman Filter to estimate wave elevation and sea state parameters, such as significant wave height, peak period, and wave direction, from noisy ship motion data. The method includes the vessel’s forward speed to better reflect real conditions and is tested on simulated ship data.
Results show that the approach can estimate the wave spectrum quickly and with reasonable accuracy, making it promising for real-time applications.

23-10-2025 - Lecture Hall E

PhD Presentation

Finding resilient operational solutions for VRP with vehicle breakdown using stochastic programming

Karel Scheepstra

Decision making in logistic processes is challenging due to several factors. For example, informed (investment) decisions over different timescales (e.g. network design, fleet properties), require estimates of utilization as a result of future demand in an unknown environmental setting. Subsequently, decision freedom is to some extent limited by the output of decisions that were made in an earlier stage (e.g. the decided network determines the available routes).
Additionally, logistic practices in today's economies of scale have been tailored to accommodate large scale logistics (e.g. through standardization (shipping containers), or simply through large transportation networks). As a result, logistic providers possess more decision freedom to find minimal cost solutions. However, at the same time this larger decision freedom also leads to optimization problems that become untractable with increasing size such as the combinatorial problems of vehicle routing and scheduling.
This work focuses on the fact that some external influences cannot be analytically represented and their impact can only be observed as the logistic operations are realized (e.g. disruptions due to weather conditions, vehicle breakdown, or network blockage). A stochastic programming approach is used to study the effect of resilient operational planning strategies on a vehicle routing problem that is subject to vehicle breakdown with respect to different objectives and constraints. The goal is to obtain insights about implementing different operational redundancies (e.g. time buffers) with respect to different problem settings.