Project description
An algorithm for personalised medication production
Compounding pharmacies are essential for creating personalised medications when standard options are not suitable, often due to patient allergies. However, these pharmacies face significant challenges in managing the production process efficiently. Different medications require unique handling, and preventing cross-contamination is crucial, leading to delays in getting treatments to patients. With the rising demand and legal requirements for timely delivery, optimising production sequencing is critical. With the support of the Marie Skłodowska-Curie Actions programme, the CompoundingPharma project aims to tackle these issues by developing a dynamic production control algorithm, using a novel heuristic inspired by the State-Dependent Riccati Equation. This innovation is designed to streamline operations, reducing delays and improving access to personalised medications.
Objective
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- social sciencessociologygovernance
- medical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugs
- medical and health sciencesclinical medicineallergology
- natural sciencescomputer and information sciencesartificial intelligenceheuristic programming
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Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
40003 Segovia
Spain