迭代调度修改的引导式探索：铁路牵引单元调度的设计研究
Guided Exploration of Iterative Schedule Modifications: A Design Study on Railway Traction Unit Scheduling
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中文总结 AI 辅助
该研究针对铁路牵引单元调度中交汇序列空间指数增长的问题，提出结合可视化、仿真与三级引导的交互式探索方法，可大幅减少调度修改的识别与评估时间精力。
中文摘要 AI 辅助
大型铁路网络中的牵引单元调度涉及复杂的运营约束：多目标优化在理想假设下生成可行的周转计划，而需要通过仿真评估其在实际运营条件下的鲁棒性。一种关键的优化机制依赖于交汇操作，即位置相近的牵引单元交换剩余调度以减少延误传播。然而，可能的交汇序列空间呈指数级增长。现有工具在识别有前景的候选方案、评估其影响及管理探索过程方面支持有限。我们提出一种交互式可视化探索方法，该方法紧密结合调度可视化、基于仿真的评估及三级引导机制，以支持牵引单元周转计划的系统性探索与交互式优化。系统以领域熟悉的形式渲染周转计划，并整合仿真结果，直接在规划场景中呈现延误传播情况。三级引导框架在概览层面按空间聚合交汇候选方案，并根据其对关键绩效指标（KPIs）的估计影响对其排序，同时在细节层面呈现每个候选方案的详细评估，以支持明智决策。应用交汇修改会触发自动调度重新计算与重新仿真，基于溯源的历史机制支持对替代修改路径的非线性探索。我们通过真实世界用例场景验证了该方法，并报告称，与当前工作流程相比，其在识别和评估有前景的调度修改所需的时间与精力上实现了大幅减少。
英文摘要
Traction unit scheduling in large railway networks involves complex operational constraints: multi-objective optimization produces feasible circulation plans under ideal assumptions, while simulation is required to assess their robustness under realistic operating conditions. A critical refinement mechanism relies on crossing operations, in which co-located traction units exchange their remaining schedules to reduce delay propagation. The space of possible crossing sequences, however, grows exponentially. Existing tools provide limited support for identifying promising candidates, evaluating their impact, and managing the resulting exploration. We present an interactive visual exploration approach that tightly couples schedule visualization, simulation-based evaluation, and a three-level guidance mechanism to support the systematic exploration and interactive optimization of traction unit circulation plans. The system renders the circulation plan in its domain-familiar form and integrates simulation results to expose delay propagation directly within the planning context. A three-level guidance framework aggregates crossing candidates spatially and ranks them by estimated impact on key performance indicators (KPIs) at an overview level, while exposing detailed per-candidate evaluation at a detail level to support informed decision-making. Applying a crossing change triggers an automatic schedule recomputation and re-simulation, with a provenance-based history mechanism enabling the non-linear exploration of alternative modification paths. We demonstrate the approach through real-world use case scenarios and report substantial reductions in the time and effort required to identify and evaluate promising schedule modifications compared to the current workflow.
