【摘要】 为提升复杂环境下铁路建设工程物流设施网络的可靠性，首先，使用情景削减技术生成最小中断情景子集及其中断概率，以描述运输通道的中断情景；然后，采用多面体不确定集刻画物流需求的不确定性，以运输成本、建设成本、运营成本以及惩罚成本等综合成本最低为目标，应用两阶段的随机优化技术与鲁棒优化技术，构建物资储备基地选址不确定优化模型，并基于一种列与约束生成(C&CG)算法求解模型；最后，以复杂环境下的C铁路建设工程为例，验证模型和算法的有效性。结果表明：在随机中断情景中，模型获取方案的成本变异系数是传统模型的4.3%,在极端需求波动情况下，模型获取方案的成本波动幅度可达传统模型的38%,两阶段不确定优化模型能有效减少物流设施网络因运输通道中断及需求波动导致的成本变动。更多还原

【Abstract】 In order to improve the reliability of the logistics facility network of railway construction projects in complex environments, scenario reduction techniques were used to generate a minimum subset of disruption scenarios and their disruption probabilities to describe the disruption scenarios of transport channels. The polyhedral uncertainty sets were used to describe the uncertainty of logistics demand. To minimize the combined costs of transport, construction, operation and penalty costs, a two-stage stochastic and robust optimisation technique was applied to construct an uncertainty optimisation model for the location of material reserves bases. The model was solved based on a C&CG algorithm. The validity of the model and the algorithm was verified by taking a C railway construction project in a complex environment as an example. The results show that the cost variation coefficient of the model-acquired solutions is 4.3% of the traditional model in the random disruption scenario, and the cost fluctuation of the model-acquired solutions can be up to 38% of that of the traditional model in the extreme demand fluctuation. The two-stage uncertainty optimisation model given in this paper can effectively reduce the cost variation of the logistics facility network resulting from the disruption of transport channels and demand fluctuations.更多还原