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

2026-03-31

AI Summary

This study addresses the multi-objective optimization challenge faced by blood collection centers in efficiently allocating multi-site donation invitations while satisfying blood type demands, ensuring donor safety and convenience, and preventing donor fatigue. The authors propose the first unified optimization framework that integrates donor eligibility, geographic accessibility, blood type requirements, safety constraints, and forward-looking demand forecasting. Combining binary integer linear programming (BILP) with an efficient greedy heuristic, the framework dynamically generates scalable donor invitation schedules. Empirical results from the Lisbon region demonstrate that the approach achieves an 86.1% demand fulfillment rate; the greedy algorithm accelerates computation by 115× and reduces memory usage by 188× compared to BILP, at a modest performance cost of only 3.9 percentage points, thereby confirming its feasibility for large-scale deployment and highlighting the critical role of reactivating dormant donors in closing supply-demand gaps.

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.

Problem

• blood donation
• donor assignment
• supply-demand matching
• multi-site scheduling
• donor fatigue

Innovation

• donor scheduling
• blood donation optimization
• greedy heuristic
• multi-site allocation
• constraint-aware invitation