arXiv:2603.29643cs.AI2026-03

用优化算法精准匹配献血者与采血时段,提升供需匹配效率。

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

  • 构建兼顾血型需求、距离便利与安全的多站点献血邀请优化框架
  • 贪心算法实现90%血型目标达成,速度比精确算法快115倍
  • 适合血站运营决策者,尤其关注低频献血者唤醒与资源调度

血站面临供需匹配难题,过度邀约易导致献血者疲劳。本文提出一个可扩展的决策支持框架,用于在多站点网络中为献血者分配采血时段,综合考虑献血者资格、地理便利性、血型需求目标及安全因素。采用葡萄牙血液与移植研究所(IPST)里斯本地区4个月的注册数据进行评估,对比了二元整数线性规划(BILP)与高效贪心启发式两种策略。结果表明,贪心算法在188倍更低峰值内存和115倍更快运行时间下,仍能实现86.1%的需求满足率(较BILP的90.0%低3.9个百分点),同时增加献血者平均行程距离、更高不良反应暴露风险及非高频献血者邀请负担。通过约束感知调度,有效激活合格但已中断献血的潜在人群,显著缩小供需差距。

原文摘要 · Abstract (English)

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.

献血优化运筹决策多站点调度启发式算法

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