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摘要: 为解决港口群系统规模、结构和布局的全局动态协调优化问题, 基于复杂系统理论中的多智能体模拟方法, 构建了港口群系统的双层规划模型, 设计了多智能体遗传算法对模型进行优化求解。建立全局优化智能体以实现港口群各智能体间的协同优化, 引入港口规模效应系数, 构建了动态腹地中各港口的货运量分担模型, 应用于下层港口智能体转换规则中。计算结果表明: 60次迭代后, 模型趋于最优解, 港口群总效益增加了48%, 结构和规模趋于合理, 表明模型和算法具有可行性和高效性。Abstract: In order to roundly optimize the scale, structure and layout of port cluster system, improve their dynamic harmony, a double planning model was built by using multi-agent simulation method based on complex system theory, and a multi-agent(MA) genetic algorithm(GA) was proposed. Global optimization agent was established to achieve the synergic optimization among the agents of port cluster system, the scale effect factor of port was introduced, an allocation model of freight volume in the hinterland of port was constructed and applied in the switching rules of port agents. Computation result shows that the model tends to the optimal solution after the 60th iteration, the total benefit of the system increases by 48%, its structure and size become rational, so the model and algorithm are feasible.
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表 1 港口信息
Table 1. Information of ports
编号 名称 吞吐量/104t 编号 名称 吞吐量/104t 1 南京港 10 090.56 7 南通港 10 386.17 2 无锡港 5 739.39 8 连云港 7 232.20 3 常州港 1 606.57 9 盐城港 1 450.75 4 长熟港 2 580.71 10 扬州港 1 767.14 5 太仓港 2 251.02 11 镇江港 6 412.35 6 张家港 10 252.92 12 泰州港 2 787.08 
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表 2 腹地信息
Table 2. Information of hinterlands
编号 名称 GDP值/亿元 货运量/104t 编号 名称 GDP值/亿元 货运量/104t 1 南京 2 587.15 10 686.3 7 连云港 695.12 3 843.0 2 无锡 3 965.67 8 421.0 8 淮安 1 733.76 4 187.0 3 徐州 1 796.50 6 114.0 9 盐城 1 004.90 7 630.0 4 常州 1 630.57 6 031.8 10 扬州 1 422.55 4 433.0 5 苏州 6 838.50 10 400.0 11 镇江 1 340.88 4 574.0 6 南通 2 552.11 8 058.0 12 泰州 1 414.41 2 123.0
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表 3 港口规模等级
Table 3. Levels of ports scales
港口 规模/104t 等级 港口 规模/104t 等级 南京港 10 295 1 南通港 8 195 2 无锡港 8 120 2 扬州港 2 500 3 张家港 8 553 2 镇江港 5 194 2 长熟港 3 500 3 泰州港 2 266 3 太仓港 3 000 3 常州港 2 500 3
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