This paper develops a data-driven, constraint-based optimization framework for a complex industrial job shop scheduling problem variant in pharmaceutical manufacturing. The formulation captures fixed routings and designated machines, explicit resource calendars with weekends and planned maintenance, and campaign sequencing through sequence-dependent cleaning times derived from site tables. The model is implemented with an open source constraint solver and evaluated on deterministic snapshots from a solid oral dosage facility under three objective formulations: makespan, makespan plus total tardiness, and makespan plus average tardiness. On three industrial instances of increasing size (10, 30, and 84 jobs) the proposed schedules dominate reference plans that solve a simplified variant without the added site rules. Makespan reductions reach \(88.1\%\), \(77.6\%\), and \(54.9\%\) and total tardiness reductions reach \(72.1\%\), \(58.7\%\), and \(18.2\%\), respectively. The composite objectives further decrease late job counts with negligible makespan change on the smaller instances and a modest increase on the largest instance. Optimality is proven on the small case, with relative gaps of \(0.77\%\) and \(14.92\%\) on the medium and large cases under a fixed time limit. The results show that a compact constraint programming formulation can deliver feasible, transparent schedules that respect site rules while improving adherence to due dates on real industrial data.

翻译：本文针对制药制造中一个复杂的工业作业车间调度问题变体，开发了一种数据驱动、基于约束的优化框架。该模型刻画了固定工艺流程与指定设备、包含周末和计划维护的显式资源日历，以及通过源自现场表格的序列依赖清洁时间实现的批次排序。模型采用开源约束求解器实现，并在一个固体口服制剂设施的确定性快照上，针对三种目标函数进行评估：完工时间、完工时间加总延迟时间、以及完工时间加平均延迟时间。在三个规模递增（10、30和84个作业）的工业实例上，所提出的调度方案优于那些求解未添加现场规则的简化变体的参考计划。完工时间减少分别达到 \(88.1\%\)、\(77.6\%\) 和 \(54.9\%\)，总延迟时间减少分别达到 \(72.1\%\)、\(58.7\%\) 和 \(18.2\%\)。复合目标进一步减少了延迟作业数量，在较小实例上完工时间变化可忽略不计，在最大实例上完工时间略有增加。在小型实例上证明了最优性，在固定时间限制下，中型和大型实例的相对间隙分别为 \(0.77\%\) 和 \(14.92\%\)。结果表明，一个紧凑的约束规划模型能够生成可行、透明的调度方案，该方案在遵守现场规则的同时，基于真实工业数据提高了对预定交货期的遵从度。