Abstract: Shipping companies are making an unprecedented effort to reduce fuel consumption and carbon emissions during the voyage, which are directly effected by port’s berth allocation plan. Considering the ship’s arrival time is a key parameter in the formulation of berth allocation plan for ports, this paper introduces the ship’s arrival time into the traditional BAP（Berth Allocation Problem） model as a decision variable, designs a new berth allocation strategy—VAT（Variable Arrival Time） strategy, which is based on the coordination between the ports and the ship operators, blends the fuel consumption and carbon emission in the BAP model’s target function and builds bi-objective（minimizing fuel consumption and departure delay time） optimization model, uses MOGA（Multi-Objective Genetic Algorithms） to solve the model and validates the strategy by a simulation example. The calculation indicates that VAT strategy can reduce shipping companies’ fuel consumption and carbon emission greatly. At the same time, it can also improve the service level of the ports and cut down the waiting time of the ships.

Key words: low-carbon economy, berth allocation plan, genetic algorithm, Variable Arrival Time（VAT） strategy

摘要： 航运公司正在进行前所未有的努力以减少船舶的燃油消耗量及碳排放量，而港口所制定的泊位分配计划对于船舶的油耗量和碳排放量有着直接的影响。由于船舶的到港时间是港方制定泊位分配计划的关键参数，因此将船舶到港时间作为决策变量引入传统的泊位分配（BAP）模型中，设计了港口与船方协调调度的新的泊位分配策略——VAT（Variable Arrival Time）策略，同时将船舶油耗和碳排放量融入BAP 模型的目标函数中，建立了船舶油耗量最小和船舶离港延迟时间最短的双目标优化模型。采用多目标遗传算法对该模型进行求解，并用仿真算例验证了该策略的有效性。计算结果表明，VAT策略可以大大削减航运公司的燃油消耗和船舶的碳排放，同时可以提高港口的服务水平，缩短船舶在港等待时间。

关键词: 低碳经济, 泊位分配, 遗传算法, 可变的到港时间（VAT）策略