Optimizing Donor Outreach for Blood Collection Sessions: A Scalable Decision Support Framework

Abstract

Blood donation centers face challenges in matching supply with demand while managing donor availability. Although targeted outreach is important, it can cause donor fatigue via over-solicitation. Effective recruitment requires targeting the right donors at the right time, balancing constraints with donor convenience and eligibility. Despite extensive work on blood supply chain optimization and growing interest in algorithmic donor recruitment, the operational problem of assigning donors to sessions across a multi-site network, taking into account eligibility, capacity, blood-type demand targets, geographic convenience, and donor safety, remains unaddressed. We address this gap with an optimization framework for donor invitation scheduling incorporating donor eligibility, travel convenience, blood-type demand targets, and penalties. We evaluate two strategies: (i) a binary integer linear programming (BILP) formulation and (ii) an efficient greedy heuristic. Evaluation uses the registry from Instituto Português do Sangue e da Transplantação (IPST) for invite planning in the Lisbon operational region using 4-month windows. A prospective pipeline integrates organic attendance forecasting, quantile-based demand targets, and residual capacity estimation for forward-looking invitation plans. Results reveal its key role in closing the supply-demand gap in the Lisbon operational region. A controlled comparison shows that the greedy heuristic achieves results comparable to the BILP, with 188x less peak memory and 115x faster runtime; trade-offs include 3.9 pp lower demand fulfillment (86.1% vs. 90.0%), larger donor-session distance, higher adverse-reaction donor exposure, and greater invitation burden per non-high-frequency donor, reflecting local versus global optimization. Experiments assess how constraint-aware scheduling can close gaps by mobilizing eligible inactive/lapsing donors.

Figures (13)

• Figure 1: Strategic donor outreach framework enabled by binary integer linear programming.
• Figure 2: Blood donations vs. Demand in the SL region (2020). The dashed line represents 100% of demand met. Consumption of units of several blood types consistently exceed donations.
• Figure 3: Distribution of last-donation recency prior to January 2020, restricted to donors who were age-eligible to donate in 2020. Donors are grouped into active, lapsing and inactive categories based on recency of their last donation; HF denotes high-frequency donors. A substantial portion of the pool had not donated for more than 10 years, indicating considerable theoretical reactivation potential.
• Figure 4: Potential vs. observed demand fulfillment in the SL region (2020). The red line shows observed monthly fulfillment based on actual collections, while the green dashed line shows the simulated BILP result after targeted outreach to eligible inactive/lapsing donors. Under the assumed 5% attendance probability, the donor pool appears sufficient to close the regional supply gap.
• Figure 5: Forecast-based demand targets. Panels A-B show pooled prior-year same-month carry-forward performance for CE and CPP forecasting. Panel C shows that carry-forward forecasting error is lowest in the high-volume blood groups and much higher in rare groups. Panel D shows achieved monthly coverage for trend-corrected quantile targets across blood types and quantile levels $\alpha$: higher $\alpha$ gives a more conservative target, but the coverage gains are uneven across blood types.