Optimizing Blister Packaging Design for Solid-Form Pharmaceuticals

Abstract

This study addresses an integrated optimization problem in pharmaceutical blister packaging operations, combining three interdependent decision layers: packaging design, machinery and mold investment, and longterm production planning. These decisions are critical for balancing material efficiency, cost-effectiveness, and operational feasibility under time-varying demand. While existing literature typically treats these components in isolation, we propose a novel mixed-integer programming model that captures their interactions within a unified framework. Solving this problem exactly is computationally challenging for real-world instances; thus, we develop a tailored metaheuristic approach based on Adaptive Large Neighborhood Search (ALNS), enhanced with problem-specific heuristics and local search. Computational experiments on realistic data from a leading Turkish pharmaceutical company demonstrate the effectiveness of our approach in generating high-quality solutions across different planning horizons and demand scenarios. Additionally, our model supports sustainability goals by reducing raw material usage in packaging design, contributing to lower carbon emissions during production.