Preface In the wake of the economical crisis of 2008, the shipping industry changed from a very profitable industry to a struggling one, aiming to optimize vessel operations in order to survive. Theories regarding route optimization based on weather and oceanic currents exist, but only few reliable industrial applications can be found. This, together with nowadays global environmental concerns, is where the roots of this project, aiming at developing a route optimization tool for seagoing vessels, based on real-life vessel data, short-term weather forecasts and oceanic currents and monthly averaged sailing conditions can be found. Due to the huge amount of fuel burned by seagoing vessels, achieving just a fraction of fuel savings already results in a significant reduction of global greenhouse gas production. The concept of weather routing is not new, but the results that can be achieved by using it are not widely documented. In order to quantify this, a weather route optimization tool has been developed. The availability of detailed hindcast datasets made it possible to incorporate monthly averaged sailing conditions in the optimization process, influencing the decision on which route to take when weather forecasts are not available anymore. Analysis of different ratios between forecasts, monthly averaged conditions and taking the shortest path to describe the sailing environment, led to the conclusion that forecasts are more reliable than sailing according to the monthly averages or taking the shortest path, as long as these forecasts are available. When no forecasts are available, using monthly averages as reference environment is favored over taking the shortest path. While evaluating randomly selected routes, it became clear that the usage of these monthly averaged sailing conditions can reduce the fuel consumption by 0.59 %, where the total effect of applying weather route optimization is found to be approximately 3.18 %. Due to the limited number of simulations performed and the unstructured nature of the data distribution, the 95 % confidence interval of the expected fuel savings ranges from 2.54 % to 4.02 %. When assuming the achieved savings are approximating reality, application of weather route optimization on the entire CMB fleet, containing close to 100 vessels, leads to a CO2 emission reduction almost 80 thousand metric tonnes per year. This is the result of a fuel consumption reduction of 23.5 thousand tons, which would roughly saves 9.35 million US Dollar in bunker costs.