Maritime decarbonization is transforming ports and shipping networks into increasingly interconnected operational and energy systems. However, operational planning, energy management, demand prediction, and uncertainty handling are often studied separately, limiting their applicability to electrified ports and emerging alternative-fuel networks. This thesis develops a layered framework for energy-aware operations planning across port and maritime systems under uncertainty, combining stochastic optimization, machine learning, and distributionally robust optimization.

At the container-terminal level, a two-stage stochastic mixed-integer programming model coordinates vessel scheduling, cargo-handling equipment, and energy management under uncertain vessel arrivals and electricity prices. A progressive hedging algorithm is developed to solve the resulting problem. Experiments based on Container Terminal Altenwerder in Hamburg show that demand-responsive planning can shift flexible electricity loads and reduce operational costs by up to 13.2% under real-time electricity pricing.

At the berth level, a data-driven predict-then-optimize framework constructs time-granular shore power demand profiles from vessel port-call records and static vessel characteristics. Machine-learning models are used to predict these profiles under different levels of information availability, after which the predictions are integrated into a shore-power-aware berth allocation model. Experiments using data from Hamburg and Hong Kong demonstrate that prediction-informed planning substantially reduces peak-capacity violations and produces solutions close to the perfect-information benchmark. The results also show that the model with the highest predictive accuracy does not necessarily yield the best downstream operational decisions, underscoring the importance of evaluating prediction and optimization jointly.

At the shipping-network level, deterministic, chance-constrained, and Wasserstein-based distributionally robust models jointly optimize sailing speed, bunkering locations and quantities, and fuel choice for dual-fuel liner services under uncertain port fuel supply. The distributionally robust approach substantially improves out-of-sample feasibility, although at moderately higher planned costs. Methanol-uptake experiments further reveal that bunkering-station coverage determines the operational potential for alternative-fuel use, while price competitiveness determines how closely that potential is realized.

Overall, this thesis demonstrates that embedding energy considerations and uncertainty management directly into operational planning can improve economic efficiency, energy utilization, and operational reliability. It contributes an integrated operations-research and data-driven perspective for supporting the transition toward lower-emission port and maritime transport systems. ... Read more

Rising energy expenses, the shift towards renewable sources, and grid congestion considerably affect the operations of container terminals. To tackle these challenges, it is necessary to implement energy-aware integrated operational planning which considers related uncertainties. This work proposes a two-stage stochastic mixed integer programming model to optimize container terminal operations planning and demand-responsive energy management. To this end, energy consumption is shifted whenever operationally possible and economically beneficial. We solve the proposed model by developing a dedicated progressive hedging algorithm. Operations considered in this model include vessel scheduling at berths, temperature control of refrigerated containers, and allocation of handling capacity of quay cranes, yard cranes, and automated guided vehicles to serve each vessel. Various scenarios for vessel arrival times and electricity prices are explored representing the uncertainty of energy demand and supply, respectively, based on a case study of the Altenwerder container terminal in Hamburg. Our results suggest potential cost savings of 5.9 per cent on average with a single energy price based on a long-term contract and 13.2 per cent when applying varying real-time electricity prices based on wholesale market rates. These findings underscore the substantial potential of demand response strategies for (electrified) container terminal operations. ... Read more

Storage space management in bulk terminals has become an important focus for research and practical operation due to the increasing demand for bulk cargo and limited storage space in stockyards. The study of storage space management in dry bulk terminals is less thorough and comprehensive, and the existing research investigates the storage space allocation problem with other operational problems like berth allocation problems, but little environmental consideration has been incorporated. We investigate the storage space allocation problem with the consideration of stacker-reclaimer assignment and mist cannon operation to deal with the dust generated during material stacking. A mixed integer programming model has been established with the aim of minimizing energy consumption to reflect the pursuit of the growing emphasis on climate-neutral operations and sustainability. We test the effectiveness of the model by conducting computational experiments. We use the commercial solver CPLEX to obtain the optimal solutions for most of the test instances. Useful managerial insights extracted from the computational results may serve as a reference for storage space management in dry bulk terminals. ... Read more