Abstract
Personalised medicine (PM) is a new area of healthcare that has shown promising results in offering treatments for rare and advanced stage diseases. Nonetheless, their supply chain differs from the traditional healthcare model by adding a high level of complexity through product individualisation. The patient is now also the donor, and becomes part of a complex manufacturing and delivery system. PM biopharmaceuticals are cryopreserved (frozen) and re-engineered at specialised facilities, before being returned to the same patient. The corresponding facility location problem (FLP) consists of both manufacturing and cryopreservation sites, having a set of constraints that are not met in other healthcare supply chains. As a result, the FLP in the context of PM has only been partially analysed and additional research is still necessary to reach optimal network configurations. In this paper, we extend the solution methods previously proposed for PM FLP by using a stage-wise approach in which the (constrained binary multi-objective) problem is divided into smaller, logical parts. We approach the problem from the perspective of the decision maker (DM) and use the R-NSGA-II algorithm to find a set of desirable solutions. By optimising only part of the decision space at a time, we reduce the complexity of the problem and allow the DM to compare the objective values obtained between different supply chain configurations. Our results suggest that allowing the DM to interact with the optimisation process can lead to good and desirable solutions in a shorter computational time, and more flexible network configurations.
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Avramescu, A., López-Ibáñez, M., Allmendinger, R. (2023). Interactive Stage-Wise Optimisation of Personalised Medicine Supply Chains. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_46
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