CompoundingPharma
Financiado
Dynamic production sequencing to minimize patient delays for compounded medicati...
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. Give...
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.
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Duración del proyecto: 24 meses
Fecha Inicio: 2024-03-14
Fecha Fin: 2026-03-31
Fecha Fin: 2026-03-31
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-03-14
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
HORIZON-MSCA-2023-PF-01-01:
MSCA Postdoctoral Fellowships 2023
Cerrada
hace 1 año
Presupuesto
El presupuesto total del proyecto asciende a
181K€
Líder del proyecto
IE UNIVERSIDAD
No se ha especificado una descripción o un objeto social para esta compañía.
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