In modern online advertising platforms, Guaranteed Delivery (GD) contracts coexist and bid with Real-Time Bidding (RTB) auctions. Recent approaches either decouple GD and RTB optimization or rely on heuristic priority rules, and thus fail to effectively balance short-term revenue maximization with long-term contract delivery under complex multi-slot delivery and impression constraints. To address these challenges, we propose HMAF (Hierarchical Multi-Slot Allocation Framework), a unified framework designed to optimize impression allocation in GD--RTB advertising platforms. HMAF employs the Plan--Calibrate--Execute paradigm as its core structure, and integrates offline constraint optimization with online decision-making, balancing offline GD resource planning, dynamically calibrating GD--RTB competitiveness, and making real-time listwise rank decisions across multi-slot environments. HMAF has been implemented in multiple marketing scenarios at Meituan, one of the world's largest online food delivery platforms, leading to a 3.72% increase in GD delivery rate and a 1.59% increase in total advertisement revenue.

翻译：在现代在线广告平台中，担保交付（GD）合同与实时竞价（RTB）拍卖共存并相互竞价。现有方法要么将GD与RTB优化解耦，要么依赖启发式优先级规则，因此无法在复杂多槽位交付和曝光约束下有效平衡短期收入最大化与长期合同交付。为应对这些挑战，我们提出HMAF（分层多槽位分配框架），这是一种专为优化GD-RTB广告平台中曝光分配而设计的统一框架。HMAF采用“规划-校准-执行”范式作为核心结构，将离线约束优化与在线决策相结合，平衡离线GD资源规划、动态校准GD-RTB竞争力，并在多槽位环境中做出实时列表级排序决策。HMAF已在全球最大在线外卖平台之一的美团多个营销场景中部署实施，使GD交付率提升3.72%，广告总收入增加1.59%。