Regular coronavirus disease 2019 (COVID-19) epidemic prevention and control have raised new requirements that necessitate operation-strategy innovation in urban rail transit. To alleviate increasingly serious congestion and further reduce the risk of cross-infection, a novel two-stage distributionally robust optimization (DRO) model is explicitly constructed, in which the probability distribution of stochastic scenarios is only partially known in advance. In the proposed model, the mean-conditional value-at-risk (mean-CVaR) criterion is employed to obtain a tradeoff between the expected number of waiting passengers and the risk of congestion on an urban rail transit line. The relationship between the proposed distributionally robust model and the traditional two-stage stochastic programming (SP) model is also depicted. Furthermore, to overcome the obstacle of model solvability resulting from imprecise probability distributions, a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form. A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming (MILP) solver is developed to improve the computational efficiency of large-scale instances. Finally, a series of numerical examples with real-world operation data are executed to validate the proposed approaches.

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Studies on the impacts of a particular land use type change are relatively rare, especially in the Tibetan Plateau region (TP). This study focused on the impacts of farmland use change on grain supply and ecosystem stability in the Yarlung Zangbo river and its two tributaries (also known as One River and Two Streams, ORTS), using net primary productivity (NPP), known as the total amount of organic matter left after removal of carbon absorbed from the atmosphere by vegetation through photosynthesis, as a common proxy for farmland productivity and ecosystem stability. The trend analysis method was applied to measure the inter-annual change of NPP, and an ecological impact index was constructed to quantify the impact of farmland use change on the NPP change in the ORTS region. The results showed that: (1) The total area of farmland decreased by 6.09% from 2000 to 2018. Built-up land occupation and ecological restoration were the main reasons for the decrease of farmland area, while there was also new reclaimed farmland, transferred from ecological land. (2) The NPP in the ORTS region was roughly on an increasing trend, while the trends of NPP in different farmland change areas were not the same. Specifically, the NPP of ecological restoration, newly reclaimed farmland, and unchanged farmland areas all showed a significant increasing trend, while the NPP in the area of farmland occupied by built-up land showed a significant decreasing trend. (3) The impact of farmland changes from 2000 to 2018 contributed 1.22% to the increase of NPP in the ORTS region. This study not only provides a research paradigm in quantifying the production and ecological impacts of a particular land use type change that can be applied in related studies in other regions, but at the same time, the results of the empirical analysis in the ORTS region can also provide suggestions for the rational use and conservation of farmland and the stability and sustainable development of ecosystems for the region and even the TP.