A number of parallel gradient-free local optimization methods are investigated in application to problems of voyage planning for maritime ships. Two optimization algorithms are investigated, a parallel version of the Nelder-Mead Simplex method and the Subplex method with Nelder-Mead Simplex as its inner solver. Additionally, two new formulations of the optimization problem are suggested which together with an improved implementation of the objective function increases the overall performance of the model. Numerical results show the efficiency of these methods in comparison with the earlier introduced Grid search method and solvers from an open-source optimization library.

Voyage planning methods have advanced significantly in recent years to take advantage of the increasingly available computing power. With the aid of detailed weather predictions it is now possible to decide a route that is optimized with respect to some criterion. With the introduction of so called All Electric Ships; ships with diesel electric propulsion, varying the power production in order to adjust the propulsion has become easier. Incorporating a power management system with the voyage planning software on a ship allows for different techniques to reduce fuel consumption. In this thesis, three different approaches are developed, compared and combined. The first method handles the task of how to optimally share a load demand across a set of generators. The second is performing power production scheduling with respect to engine efficiencies, and finally in the third the potential in energy storage integration with the power management system is investigated. From the results, it is argued that the largest potential lies in the first approach where large fuel savings can be made without any large risk. The second approach shows potential for fuel reduction but this however is found to be heavily dependent on weather predictions and accuracy of the used models. Regarding energy storage it is found that while it is not economically feasible to increase the fuel efficiency, energy storage can be used to handle load transients and fulfil power redundancy requirements.

The shipping industry is very large and ships require a substantial amount of fuel. However, fuel consumption is not the only concern. Time of arrival, safety concerns, distance travelled etc. are also of importance and these objectives might be inherently conflicting. This thesis aims to demonstrate multi-objective optimization and Pareto navigation for application in voyage planning. In order to perform this optimization, models of weather, ocean conditions, ship dynamics and propulsion system are needed. Statistical methods for estimation of resistance experienced in calm and rough sea are used. An earlier developed framework is adopted to perform the optimization and Pareto navigation. The results show that it is a suitable approach in voyage planning. A strength of the interactive Pareto navigation is the overview of the solution space presented to the decision maker and the control of the spread of the objective space. Another benefit is the possibilities of assigning specific values on objectives and setting thresholds in order to narrow down the solution space. The numerical results reinforces the trend of slow steaming to decrease fuel consumption.