Dynamic production sequencing to minimize patient delays for compounded medications

Descripción del proyecto

Un algoritmo para la producción de medicamentos personalizados

Las farmacias que elaboran fórmulas magistrales son fundamentales para crear medicamentos personalizados cuando las opciones convencionales no son adecuadas, a menudo debido a las alergias de los enfermos. Sin embargo, estas farmacias tienen que hacer frente a retos importantes a la hora de gestionar de forma eficaz el proceso de producción. Los distintos medicamentos requieren una manipulación única, y evitar la contaminación cruzada es esencial, lo cual provoca retrasos en la administración de los tratamientos a los enfermos. El aumento de la demanda y los requisitos legales de entrega a tiempo hace esencial optimizar el proceso de producción. En el proyecto CompoundingPharma, que cuenta con el apoyo de las acciones Marie Skłodowska-Curie, se pretende resolver estas cuestiones mediante el desarrollo de un algoritmo de control dinámico de la producción. Para ello, se empleará una novedosa heurística inspirada en la ecuación de Riccati dependiente del estado. Esta innovación está diseñada para agilizar las operaciones, reducir los retrasos y mejorar el acceso a medicamentos personalizados.

Objetivo

When standard medications are not appropriate for a patient’s needs, e.g. due to their allergies, retail pharmacies must rely on compounding pharmacies to blend raw ingredients and produce a personalized medication to order. Given the growth in demand for personalized medications and the legal mandate in some countries that compel pharmacies to provide such medications to patients, the efficient operation of compounding pharmacies is critical to timely access to medications. However, there are complex operational dynamics when sequencing production driven by differences in medications and the need to prevent cross-contamination that lead to production delays for patients. Inspired by discussions with the management team of a compounding pharmacy, this project aims to improve operational efficiency and reduce delays by developing a dynamic production control algorithm that sequences medication production. The challenge of identifying optimal policies for multi-product production systems in the presence of set-up times has lead researchers to focus on heuristics, however, existing policies do not account for the sequence-dependent set-up times or batch processing in this setting. We propose and evaluate a theory-driven heuristic based on a novel modification of an optimal control engineering technique, known as the State-Dependent Riccati Equation approach. The performance of this heuristic is to be evaluated both theoretically as well as numerically relative to alternative heuristic policies via simulation. This contributes to the operations management and management literature, through the development and analysis of an innovation in production governance, a critical component of the industrial value chain in this setting. Moreover, the proposed algorithm can be modified for use in other complex production settings where optimal policies are intractable, and decision-makers must rely on heuristics.