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在枢纽网络设计时,未来的成本和需求等参数具有不确定性.为了使设计的网络能在各种情景下具有最优的期望成本,提出了无容量限制的多分配严格p-枢纽中位随机优化模型.考虑到模型本身的结构特点和复杂程度,采用了PH分解算法结合增广拉格朗日松弛算法,将原问题转化为若干个独立子问题来求解.使用了基于经典算例的随机数据集合对模型和算法进行了测试,算例结果表明尤其在情景数量较大的情况下,算法体现出较高的效率.同时,通过随机解价值分析了使用随机优化模型对于该算例的意义.
In the design of hub network, the future cost and demand parameters are uncertain.In order to make the designed network have the optimal expected cost in all kinds of scenarios, a multi-allocation strict p-hub without capacity limitation Stochastic optimization model.According to the structural characteristics and complexity of the model, the PH decomposition algorithm and the augmented Lagrangian relaxation algorithm are adopted to solve the original problem into several independent sub-problems.According to the classical example The results show that the algorithm shows a high efficiency especially when the number of scenarios is large.At the same time, by analyzing the value of stochastic solution, we use the stochastic optimization model to analyze the model and the algorithm significance.