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摘要: 为了避免运输网络内港口重复建设, 实现港口资源的优化配置, 综合考虑港口规划者与港口使用者之间的相互作用及决策均衡, 以Stackelberg模型为基础, 建立了区域内集装箱港口网络布局规划优化模型。为了有效地反映港口使用者个体的特殊性, 采用非集计理论, 从其自身属性出发, 分析了微观货主的选择行为。以各港口的泊位数和相应航线班期密度组成的矩阵作为染色体的一个基因串, 考虑模型约束的特殊性, 设计了模型求解的遗传算法。模拟实例计算结果表明: 在某运输网络内, 模拟得到的港口网络布局规划的近似最优解符合实际分析结果, 能表明货物运输主体的行为特性和影响因素, 利用该模型有利于实现港口网络内资源的优化配置。
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关键词:
- 交通规划 /
- 集装箱港口网络 /
- Stackelberg模型 /
- 遗传算法 /
- 非集计理论
Abstract: In order to avoid the repetitive construction of container ports in transportation network, and realize the optimization distribution of ports, the general interaction and decision equilibrium between container port planner and user according to Stackelberg model were considered, and an optimization model of network distribution planning on regional container ports was established. In order to effectively reflect the individual properties of port users, the choice behaviors of micro-shippers were analyzed by using disaggregation theory according to their attributes. A genetic algorithm was designed to solve the model, its initial chromosome was generated by the matrix composed with the numbers of berths and corresponding voyage densities of different container ports, and the particularity of the model restrictions was considered. Example simulation result shows that the model is feasible, the approximative optimization port network planning of example computation accords with real analysis situation, the behaviors and influence factors of shippers can be reflected, and port optimization distribution can be realized. -
表 1 非集计模型估计结果
Table 1. Estimation result by using disaggregation model
选择变量 估计系数 标准差 t检验 选择枝常数项 港口1 -0.394 9 0.310 3 -1.272 8 港口2 1.585 5 0.318 7 4.974 7 港口3 0.868 1 0.336 1 2.583 1 特性变量 全程运输时间 -2.181 8 0.233 3 -9.351 5 全程运输成本 -0.009 48 0.001 19 -7.941 90 统计结果 θk为0时, 对数极大似然函数L(0)为-1 052.2, 最大似然函数为-287.97, 观测样本数为759, 命中率为83.926%, 极大似然比ρ2为0.726 3 
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表 2 集装箱港口网络布局规划计算结果
Table 2. Computation result of container port network planning
港口1 港口2 港口3 港口4 D1 D2 D3 D1 D2 D3 D1 D2 D3 D1 D2 D3 规划泊位 1 6 3 2 班期密度/(班·周-1) 2 2 2 16 17 9 8 8 5 3 4 7 货流分配/104 TEU O1 0.77 0.82 1.27 11.73 12.51 8.65 8.23 8.70 5.49 1.62 1.73 2.69 O2 0.10 0.11 0.16 20.90 22.26 15.21 0.52 0.50 0.29 0.10 0.11 1.05 O3 0.09 0.10 0.33 2.65 2.80 1.71 7.86 8.32 5.07 0.47 0.55 1.86 O4 3.47 3.74 3.46 2.82 3.04 1.86 1.11 1.06 0.63 0.13 0.15 0.14 O5 0.07 0.08 0.07 2.06 2.25 1.38 2.99 2.86 1.69 7.62 8.35 7.17 O6 1.10 1.24 2.04 13.00 14.64 8.63 12.47 11.85 7.62 2.56 3.23 5.32
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