With the daily use of liquid cargoes such as crude oil and their derivatives, the global transportation of liquid cargoes has developed rapidly. Liquid cargoes are mainly transported via tankers and pipelines. In the liquid terminal, the handling operations and internal transportation operations are conducted using oil transfer arms and pipelines, and the pipeline path of the cargo is selected using valves. The number of times a valve opens and closes and the length of pipeline paths are the main factors that affect handling time and cost. In addition, different types of valves have different operating costs and levels of operating energy consumption. At this stage, most of the valve selection work is still manually completed, which consumes a lot of time and generates high labor costs, and the actual operation efficiency is low. In this paper, the cargo unloading pipeline path is the main research object, the problem of oil transfer arms–valves–pipeline (PAVP) is proposed, and a dual-objective model is established, accounting for total time in port and the unloading cost of the vessel. An NSGA-II-Dijkstra hybrid algorithm is employed to solve the PAVP, and the improved algorithm (INIIDA) is designed to improve the solution speed via an adaptive dynamic probability based on the Pareto level and heaps in the shortest path. The results show that the INIIDA could better address the PAVP than the NSGA-II-Dijkstra hybrid algorithm. Innovative fusion algorithms are employed to improve the efficiency of port operations.

Driven by the surging global demand for crude oil and its byproducts, liquid tanker vessels have undergone a marked shift toward ultra-large dimensions. This growth, while enhancing transport capacity, has also intensified congestion across many liquid terminals. As the Dead Weight Tonnage (DWT) of vessels rises, so does their draft, often requiring tide-dependent navigation for safe entry into ports. To address the resulting operational complexities, this study investigates the coordinated scheduling of three critical resources—channels, tugboats, and berths—at liquid terminals. A novel optimization framework, termed the Channel-Tugboat-Berth-Tide (CUBT) model, is proposed. The primary objective is to minimize the total operational cost over a planning horizon, accounting for anchorage waiting time, channel occupancy, tugboat utilization, and penalties from delayed departures. To solve this model efficiently, we adopt an enhanced variant of the Logistic-Hybrid-Adaptive Black Widow Optimization Algorithm (LHA-BWOA), incorporating Logistic-Sine-Cosine Chaotic Map (LSC-CM) initialization, hybrid reproduction mechanisms, and dynamic parameter adaptation. A series of case studies involving varying planning cycles are conducted to validate the model’s practical viability. Furthermore, sensitivity analyses are performed to evaluate the impact of channel choice, tugboat allocation, and vessel waiting time. Results indicate that tugboat operations account for the largest portion of the total costs. Notably, while two-way channels result in lower direct channel costs, they do not always yield the lowest overall expenditure. Among the service strategies evaluated, the First-In–First-Out (FIFO) rule is found to be the most cost-efficient. The results offer practical guidance for port improving the operational efficiency of liquid terminals under complex tidal and resource constraints.

To achieve a balance between operational effectiveness and service efficiency in container terminals has become a critical issue with the rapid growth of container trade. In this paper, the scheduling problem of frontier operation resources of container terminals is proposed and studied. And a scheduling optimization method is proposed for the container terminal frontier operation resources. Considering the mutual influence between multiple resources, a joint scheduling optimization model of container terminal frontier operation resources is established. A multi-objective fusion algorithm is designed to carry out the solution.