Uncertainty is critical in bulk terminals because it is inherent to many operations. In particular, the berth allocation problem (BAP) is greatly affected by the uncertain arrival times of the vessels. In this paper, we propose the first distributionally robust optimization (DRO) model for the...

This study introduces a hybrid feature selection technique with a multi-objective algorithm incorporating Information Gain, Random Forest, and Relief F-based approach. We integrate the strengths of filter and wrapper methodologies to enhance the efficacy of addressing feature selection. The...

Metaheuristics are widely used to address complex optimisation problems where traditional exact methods are computationally infeasible or insufficiently flexible. With the rapid advancement of artificial intelligence, large language models, such as ChatGPT, Claude, Gemini, and LLaMA, have...

Mass rapid transit systems around the world are typically designed for all-stop operation schemes, in which train overtaking is not possible. To accelerate transit operation, the conventional A/B skip-stop scheme may be planned. This research explores alternative skip-stop schemes with three...

The increasing penetration of renewable sources in the energy market enhances sustainability but also introduces variability, driven by climate dynamics, that can negatively affect stakeholders (private investors, consumers, regulators, etcetera). For firms considering new renewable energy...