Data-driven Modelling and Control of Hospital Blood Inventory

Determining order quantities for platelet and Red Blood Cell (RBC) units at hospitals is a challenging task since they are perishable and their usage is subject to high uncertainty. In this thesis, we develop data-driven models and methods for determining order quantities for hospitals that order their required units from a central supplier such as Canadian Blood Services (CBS). An important characteristic of the problem in the case of such hospitals is that the remaining age of received units is also subject to uncertainty, a factor that has been generally ignored in the literature. We use data from a network of hospitals in Hamilton, ON, to develop and test our proposed models. We study a periodic-review perishable inventory problem with zero lead-time that operates under the Oldest-Unit, First-Out (OUFO) allocation policy. In Chapter 4, we propose a data-driven approach for determining daily order quantities for platelets. Our approach can be viewed as a demand prediction model that is equipped with a new loss function. Specifically, we assume the required inventory (base-stock) level for each period is a linear function of a set of observed features in that period and optimize coefficients of the linear model by minimizing an approximate measure of the inventory costs comprised of expiry and shortage costs. Our first model assumes a fixed remaining age tuned using cross validation while our second model explicitly accounts for the remaining age variability through a robust optimization approach. In Chapter 5, we introduce two extensions of our first model to account for holding costs, particularly tailored for ordering RBCs that have a longer shelf-life. Our data-driven approach however does not account for fixed ordering costs and therefore is appropriate for larger hospitals relying on daily deliveries. In Chapter 6, we consider a dynamic programming (DP) approach using parametric models of demand and remaining age uncertainty and propose a simulation-based Approximate Dynamic Programming (ADP) for general stochastic perishable inventory problems with fixed ordering costs and decision-dependent remaining age uncertainty. We leverage ADP-based policies to investigate the value of accounting for (decision-dependent) uncertainty in the shelf-life of units when making ordering decisions.

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