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Ronghui Liu

Publications and source records attributed to Ronghui Liu.

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Train Unit Scheduling with Unit Ordering under Platform-Feasible Operations

In passenger railways where coupling and decoupling occur at platforms, a rolling-stock plan may be circulation-feasible but station-infeasible when the within-formation order of identified units causes blockage. We study the Train Unit Scheduling Problem under platform-feasible operations, where units cannot overtake or be resequenced without authorised shunting or resequencing. We formulate, to the best of our knowledge, the first single-stage unit-level integer linear programming model for this setting. It tracks identified units on a unit-indexed connection network and jointly determines the movements, coupling and decoupling decisions, and within-formation positions of train units, so every feasible integer solution provides a blockage-free schedule under the modelled restrictions. We further derive an exact fixed-assignment characterisation of orderability. Active coupling and decoupling requirements induce trip-wise precedence digraphs, while continuation arcs impose pairwise carry-over consistency. An assignment is orderable if and only if these digraphs admit a continuation-consistent family of topological orders. This yields a Train Unit Scheduler with Ordered Units (TUSOU), an exact branch-and-bound-and-cut train unit scheduler developed by us using ordering certification, lazy recovery of ordering constraints and activated cycle inequalities. Experiments on five real-world-derived TransPennine Express instances show that TUSOU produces certified blockage-free schedules, solves all instances to zero reported gap under solver tolerances, and outperforms direct full-model Gurobi baselines. Certification rejects 39 of 59 integer assignment-candidate encounters, showing that orderability should be embedded in optimisation rather than treated as post-processing.

math.OC

Study on departure time choice behavior in commute problem with stochastic bottleneck capacity: Experiments and modeling

Uncertainty is inevitable in transportation system due to the stochastic change of demand and supply. It is one of the most important factors affecting travelers' choice behavior. Based on the framework of Vickrey's bottleneck model, we designed and conducted laboratory experiment to investigate the effects of stochastic bottleneck capacity on commuter departure time choice behavior. Two different scenarios with different information feedback are investigated. The experimental results show that the relationship between the mean cost (E(C)) and the standard deviation of cost (\sigma) can all be fitted approximately linearly with a positive slope \sigma=E(C)/\lambda^*-m (\lambda^*>0). This suggests that under the uncertain environment, travelers are likely to minimize their travel cost budget, defined as E(C)-\lambda^* \sigma, and \lambda^*>0 indicates that the travelers behave risk preferring. The experiments also found that providing the cost information of all departure times to the commuters lowered the commuters' risk preference coefficient (i.e., \lambda^* decreases). We propose a reinforcement learning model, which is shown to reproduce the main experimental findings well.

physics.soc-ph