Data-Driven Optimisation for the Development and Delivery of Personalised Medicine

Student thesis: Phd


The biopharmaceuticals developed under Personalised Medicine (PM) are the most promising medical treatments of this century, yet their commercialisation on large scale remains sub-optimal. The manufacturing and delivery of advanced therapy medicinal products created for individual patients rather than population groups are highly affected by worsened bottlenecks, such as low global demand, low shelf-lives, or increased product fragility. The current pharmaceutical supply chain configurations are optimised for mass delivery and, hence, they lead to long waiting times and high costs per patient in the delivery of PM, where each product corresponds to one exact patient. This thesis argues for the immediate necessity of new decision-making frameworks adaptable to the requirements of PM. We compare the bottlenecks encountered in the new personalised medical products to the most common mature supply chains of the healthcare and pharmaceutical industries, highlighting their dissimilarities. We make an initial theoretical contribution by uncovering the need for a new supply chain in light of the rapid clinical developments of personalised medical products. To address some of these challenges, we focus the rest of the thesis on the strategical level of the supply chain. Considering the expectations of the real-world, we propose several multi-objective mathematical models and solution methods for large scale facility location problems. Our formulations follow both centralised and decentralised networks and aim to find optimal locations for multiple types of interdependent facilities commonly met in the PM supply chain. The models and algorithms proposed are validated using data corresponding to products with current market approval and an estimation of global demand. The results presented throughout the thesis are consistent with the current motivations and proposed directions of how the PM supply chain should look. We show that the benefits brought by considering PM as standalone and not part of the regulations of the more traditional pharmaceuticals overcome the disadvantages. The development of specialised decision support tools can lead to smaller costs for biopharmaceutical companies, lower delivery times, and overall better global coverage. This research was in collaboration with an industrial partner, Biopharm Services, who has contributed to validating the mathematical formulations with respect to their practicality for biopharmaceutical companies and have provided part of the data used.
Date of Award31 Dec 2023
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorManuel Lopez-Ibanez (Supervisor) & Richard Allmendinger (Supervisor)


  • personalised medicine
  • supply chain
  • multi-objective optimisation
  • advanced medical therapies

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