Efficient Data-Driven Production Scheduling in Pharmaceutical Manufacturing

2026-02-14

AI Summary

This work proposes a data-driven constraint optimization framework to address the complex job shop scheduling problem in pharmaceutical manufacturing. For the first time, it integrates comprehensive real-world constraints—including dedicated equipment, resource calendars, and sequence-dependent cleaning times derived from empirical measurements—into a compact constraint programming model, enabling high-fidelity representation of actual production environments. Built upon an open-source constraint solver, the approach supports multi-objective optimization, such as minimizing makespan and total or average tardiness. Evaluated on three industrial instances, the method substantially outperforms simplified baselines, achieving up to an 88.1% reduction in makespan and a 72.1% decrease in total tardiness, while also verifying optimality on small-scale cases.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Programming
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Web Mining and Content Analysis: Web data integration and cleaning

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.

Problem

• pharmaceutical manufacturing
• job shop scheduling
• sequence-dependent setup times
• constraint-based optimization
• production scheduling

Innovation

• constraint programming
• data-driven scheduling
• pharmaceutical manufacturing
• sequence-dependent setup times
• industrial job shop