Container unloading and loading operations in ports are addressed through the Berth Allocation Problem (BAP). Developing container terminal models and methods that enhance operational efficiency is undeniably essential for supporting maritime ports in managing increasing container flows within global supply chains. Consequently, recent years have witnessed a growing body of research literature aimed at advancing quayside operations. This study first examines the theoretical framework of the Quay Crane Scheduling Problem (QCSP) and Quay Crane Assignment Problem (QCAP) as presented in existing literature. We then formally define these problems within deterministic and sequencing contexts. The research employs berth modeling alongside Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) for deterministic scenarios, while stochastic conditions are addressed through berth simulation. Given the NP-Hard nature of the problem, obtaining optimal solutions within reasonable timeframes is infeasible. Thus, we implement metaheuristic approaches—GA, PSO, and simulation of model—to allocate vessels to berths efficiently.