Main Article Content
Decreasing the fuel consumption and thus greenhouse gas emissions of vessels have emerged as a critical topic for both ship operators and policymakers in recent years. The speed of vessels has long been recognized to have the highest impact on fuel consumption. The aim of this study is to develop a speed optimization model using a time-constrained genetic algorithm (GA). Subsequent to this, this paper also presents the application of machine learning regression methods in constructing a model to predict the fuel consumption of vessels. The local outlier factor algorithm is used to eliminate outliers in prediction features. The overfitting problem is observed after hyperparameter tuning in boosting and tree-based regression prediction methods. The early stopping technique is applied for overfitted models. In this study, speed is found to be the most significant feature for fuel consumption prediction. On the other hand, GA evaluation results showed that random modifications in the default speed profile could increase GA performance and thus fuel savings more than constant speed limits during voyages.