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研究了船舶动态到港情况下的连续泊位分配问题。对Imai模型进行了分析,指出其非线性约束数量是关于到港船舶数量的二次函数,呈幂数级增长,增加了精确算法的求解难度。通过设置新的变量、重新规划时间序列与空间序列等约束条件,构建了新的混合整数非线性规划模型,有效地减少了非线性约束数量,提高了分支定界算法的求解效率。考虑到问题的NP-hard特性,设计了解决大规模问题的遗传算法。实验算例表明,与Imai模型相比,新模型在求解时间方面更具优势;而所设计的遗传算法,与LINGO软件相比,则能在合理的时间内有效解决泊位分配的大规模优化问题。
The problem of continuous berth allocation under the condition of dynamic arrival of a ship is studied. The Imai model is analyzed. It is pointed out that the number of non-linear constraints is a quadratic function with the number of arriving ships, increasing exponentially, which increases the difficulty of solving the exact algorithm. By setting new variables, re-planning constraints such as time series and space series, a new mixed integer nonlinear programming model is constructed, which effectively reduces the number of nonlinear constraints and improves the efficiency of the branch-and-bound algorithm. Considering the NP-hard characteristic of the problem, a genetic algorithm is designed to solve the large-scale problem. The experimental results show that the new model has more advantages in solving time than the Imai model. However, compared with the LINGO software, the proposed genetic algorithm can effectively solve the large-scale optimization problem of berth allocation in a reasonable time .