Various approaches can be used to solve dynamic optimization problems. For example, on the one hand, optimization algorithms can be restarted every time the problem changes. As this results in a loss of optimization progress, algorithms can on the other hand also be implemented in an open-ended way, and to adapt to changing problem data during the run. Some problem updates cause fundamental changes to the optimization scenario. This paper describes different strategies to evolve solutions for such problems with scenario changes in the context of crane scheduling operations. It shows that simply ignoring such changes has negative effects on optimizer convergence, and compares the convergence behavior of five different strategies that can be applied when switches between different scenarios occur.