Train rescheduling repairs disturbed timetables while enforcing train-path precedence, resource capacity, and delay objectives. Dynamic Discretization Discovery (DDD) avoids full time discretization by refining only time points needed to certify feasibility and optimality. We strengthen a recent MaxSAT-DDD model through two encoding changes. First, resource conflicts are encoded as time-dependent at-most-one cliques, using pairwise clauses for small cliques and a sequential counter for large cliques. Second, earliest feasible times are propagated along train paths before the first DDD iteration. We evaluate four MaxSAT variants, two SAT optimization backends, Gurobi/CPLEX MILP models, and CPLEX CP on 72 instances and three delay objectives. MaxSAT-DDD solves all stepwise instances in about 23 ms on average. MaxSAT-Default reduces rounded-cost runtime from 794 to 479 ms, and the ablation study reports up to 79.6\% runtime reduction on the common-solved subset of hard continuous track instances.

翻译：列车运行调整旨在修复受扰动的时刻表，同时遵守列车路径优先顺序、资源容量及延误目标约束。动态离散化发现（DDD）通过仅细化所需的时点来证明可行性和最优性，从而避免全时间离散化。我们通过两项编码改进强化了最新的MaxSAT-DDD模型：首先，将资源冲突编码为时间相关的至多一个（at-most-one）团，对小团采用成对子句编码，对大团采用序数计数器编码；其次，在首次DDD迭代前沿列车路径传播最早可行时间。我们在72个实例和三种延误目标上评估了四种MaxSAT变体、两款SAT优化后端、Gurobi/CPLEX混合整数线性规划模型及CPLEX约束规划模型。MaxSAT-DDD平均约23毫秒即可求解所有阶梯式实例。MaxSAT-Default将舍入成本运行时间从794毫秒降至479毫秒，消融研究在困难连续轨道实例的公共可解子集上报告了高达79.6%的运行时间缩减。