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Tugboat assistance is essential when large ships are berthing and unberthing, and requires ports to produce efficient tugboat schedules considering various constraints. Few studies optimize tugboat scheduling from multiple vectors simultaneously, however. This paper thus constructs a bi-objective, mixed-integer linear programming, green tugboat scheduling model in order to minimize the maximum completion time and total fuel consumption, which improves port service levels, decreases tugboat company operating expenditures and thus environmental pollution emissions. The proposed model also considers the time window characteristics of the tidal port. We use the non-dominated sorting genetic algorithm II (NSGA-II) framework integrating the characteristics of tugboat scheduling to solve the model. Finally, we make a case study about Guangzhou Port to validate the model and the algorithm via a comparison of results solved by NSGA-II and CPLEX solver. The Pareto fronts obtained show the trade-off relationship between the two objectives, and provide a basis for port tugboat scheduling plans. [ABSTRACT FROM AUTHOR]

This study proposes a novel low-cost and independent method to estimate five key performance indicators: ships traffic; berth utilization and allocation; handling efficiency; throughput; and oil spill risk. The results obtained through this method can be used as reference for oil port operation optimization and risk control. Performance indicators are estimated in real time by using AIS data and maritime open data instead of relying on the manually collected and unverifiable official data. These performance indicators are found to be directly related to the number of handling events and the corresponding handled quantity in the ports. Therefore, this paper first identifies handling events and records the corresponding berthing time and berth. Second, the handling efficiency and time are estimated to calculate the handling quantity of each tanker. Third, the identified handling events and the corresponding handling quantity are aggregated into port level in chronological order so as to extract the key performance indicators. Finally, data of an oil terminal in the Guangzhou are taken as a case to verify the feasibility and reliability of the proposed method. Image 1 • A novel real-time, low-cost and independent method to estimate oil port performance indicators is presented. • Ship traffic, berth utilization and allocation, handling efficiency, throughput, and oil spill risk are estimated. • A case study of Guangzhou's oil port is applied to verify this novel method. • First work on real-time oil port performance measurement using AIS and maritime open data instead of statistical data. • To support oil port operation optimization, risk analysis and control. [ABSTRACT FROM AUTHOR]