Efficient Data-Driven Production Scheduling in Pharmaceutical Manufacturing

TL;DR

This paper presents a data-driven, constraint-based optimization framework for complex pharmaceutical manufacturing scheduling, significantly improving efficiency and adherence to due dates compared to simplified models.

Contribution

It introduces a novel, open-source constraint programming approach that incorporates detailed site rules and campaign sequencing, outperforming traditional simplified scheduling methods.

Findings

Achieved up to 88.1% makespan reduction

Reduced total tardiness by up to 72.1%

Proven optimality on small instances

Abstract

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.