CAJ | 학술논문

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．
재추뉴망락설계시，미래적성본화수구등삼수구유불학정성．위료사설계적망락능재각충정경하구유최우적기망성본，제출료무용량한제적다분배엄격P-추뉴중위수궤우화모형．고필도모형본신적결구특점화복잡정도，채용료PH분해산법결합증엄랍격랑일송이산법，장원문제전화위약간개독립자문제래구해．사용료기우경전산례적수궤수거집합대모형화산법진행료측시，산례결과표명우기재정경수량교대적정황하，산법체현출교고적효솔．동시，통과수궤해개치분석료사용수궤우화모형대우해산례적의의．
The parameters of hub-and-spoke network design are usually uncertain. In order to get the minimal expectation cost of the network for all scenarios, this paper presents a stochastic uncapacitated strict p-hub median model under the uncertainties of parameters. Based on the model with its high com- plexity, the progressive hedging method combined with the augmented Lagrangian relaxation is introduced to solve the model. This algorithm can divide the primal problem into several subproblems effectively. Fi- nally, a case study based on the classical data sets shows importance of adopting stochastic optimization method solution. the effect of using the combined method, and the by the comparison over the values of stochastic