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

TL;DR

This paper presents a scalable optimization framework for scheduling blood donor invitations, balancing demand, donor convenience, and safety, with a practical evaluation showing effective supply-demand gap closure.

Contribution

It introduces a novel optimization approach combining BILP and a greedy heuristic for donor scheduling, addressing a previously unstudied operational problem.

Findings

The greedy heuristic performs comparably to BILP with significantly less memory and faster runtime.

The framework effectively closes the blood supply-demand gap in the Lisbon region.

Constraint-aware scheduling mobilizes inactive donors to improve supply.

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 Portugu\^es do Sangue e da Transplanta\c{c}\~ao (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.