Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling problem. Effectively managing these issues is essential for optimizing port operations; failure to do so can lead to substantial operational and economic ramifications, ultimately affecting competitiveness within the global shipping industry. Optimization models, encompassing both mathematical frameworks and metaheuristic approaches, offer promising solutions. Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. This study expands upon earlier foundational analyses of berth allocation, quay crane assignment, and scheduling issues, which have laid the groundwork for port optimization. Recent developments in uncertainty management, automation, real-time decision-making approaches, and environmentally sustainable objectives have prompted this review of the literature from 2015 to 2024, exploring emerging challenges and opportunities in container terminal operations. Recent research has increasingly shifted toward integrated approaches and the utilization of continuous berthing for better wharf utilization. Additionally, emerging trends, such as sustainability and green infrastructure in port operations, and policy trade-offs are gaining traction. In this review, we critically analyze and discuss various aspects, including spatial and temporal attributes, crane handling, sustainability, model formulation, policy trade-offs, solution approaches, and model performance evaluation, drawing on a review of 94 papers published between 2015 and 2024.

Container terminal scheduling requires coordinated decisions across berth allocation, quay crane assignment, and crane scheduling under persistent uncertainty in vessel arrival times and cargo handling durations. Existing deterministic models rely on fixed parameter values that are frequently violated in practice, leading to fragile schedules requiring costly reactive revision. Stochastic programming approaches provide theoretical robustness but require strong distributional assumptions and multiple optimisation solves, limiting real-time applicability. Machine learning–enhanced approaches improve prediction accuracy but typically rely on point estimates that neglect residual uncertainty. This paper proposes a Conformal Prediction Augmented Sequential Scheduling Framework that integrates distribution-free, finite-sample prediction intervals into a three-stage sequential optimisation pipeline. The Berth Allocation Problem and Quay Crane Assignment Problem are solved as mixed-integer programming models, while the final stage produces a calibrated makespan estimate using empirically derived crane productivity factors. Two XGBoost regression models predict vessel arrival delay and cargohandling time, and a split conformal wrapper calibrated at 90% nominal coverage generates prediction intervals propagated through the scheduling pipeline under optimistic, nominal, and pessimistic operational modes. The framework is evaluated using operational data from 241 vessel visits at Durban Container Terminal, South Africa, and benchmarked against a deterministic baseline, a scenario-averaging stochastic heuristic, and a conservative quantile regression baseline. Computational experiments across instances of 10–50 vessels achieve speedups of 13–31× over the stochastic benchmark while maintaining solution quality and providing infeasibility diagnostics absent from conventional approaches. These results demonstrate the practical viability of conformal prediction for uncertainty-aware integrated port scheduling.