In the context of global port digitalization, improving operational efficiency and resource coordination has become a pressing challenge. This study addresses the integrated scheduling of berth allocation and quay crane assignment under uncertainty in quay crane efficiency. A multi-objective optimization model is developed to minimize both vessel waiting time and delay-related costs, incorporating stochastic quay crane performance via a chance-constrained programming approach. The problem is formulated as a mixed-integer programming model and solved efficiently using the commercial solver Gurobi. To validate the proposed model, a series of computational experiments are conducted on both small-scale and large-scale port scenarios. Results demonstrate that the model effectively reduces time-related costs while maintaining high resource utilization, even under fluctuating operational conditions. Comparative analysis further reveals the model’s robustness and scalability, especially when the number of vessels increases or the availability of berth and quay crane resources decreases. This research provides a practical and theoretically sound framework for intelligent and resilient port scheduling. It offers valuable insights into resource coordination under uncertainty, supporting the development of smarter and more efficient port operations.