GenMatch：用于网约车微观视角订单派单的端到端生成式匹配框架

中文摘要 AI 辅助

微观视角订单派单在每个派单批次内将可用司机分配给乘客订单，对网约车平台的服务质量和运营效率至关重要。主流工业解决方案遵循模型预测、价值计算、派单匹配的多阶段范式。尽管派单质量由最终的批次级分配决定，但这些阶段优化不同的中间目标，这种跨阶段目标不一致意味着优化单个阶段不一定能提升整体派单结果。因此，我们将微观视角订单派单形式化为生成式匹配问题，提出GenMatch——一种端到端生成式匹配框架，也是首个部署到实际生产环境的此类框架。将生成式建模应用于该问题会带来三项挑战：第一，每个派单批次形成动态稀疏二分图，需要高效的结构化批次级编码；第二，替换手工设计的价值函数需要从异构反馈中学习统一的业务效用；第三，直接生成分配结果需要跟踪不断变化的匹配状态，因为每选中一对订单-司机会改变剩余可行候选者。GenMatch通过上下文感知二分图编码器、业务感知效用学习器和状态感知指针解码器解决这些挑战。在滴滴国际网约车市场的五个城市开展的大量离线评估和在线A/B测试显示，GenMatch相比竞争基准实现了持续提升，证实了其在工业订单派单中的有效性和实用性。

英文摘要

Micro-View Order-Dispatching assigns available drivers to passenger orders within each dispatch batch and is critical to the service quality and operational efficiency of ride-hailing platforms. Mainstream industrial solutions follow a multi-stage paradigm of model prediction, value calculation, and dispatch matching. Although dispatch quality is determined by the final batch-level assignment, these stages optimize different intermediate objectives. This cross-stage objective inconsistency means that improving a single stage does not necessarily improve the overall dispatch result. We therefore formulate Micro-View Order-Dispatching as a generative matching problem and propose GenMatch, an end-to-end Generative Matching framework and the first such framework deployed in a real-world production environment. Applying generative modeling to this problem introduces three challenges. First, each dispatch batch forms a dynamic sparse bipartite graph, requiring efficient structured batch-level encoding. Second, replacing the hand-crafted value function requires learning unified business utility from heterogeneous feedback. Third, directly generating an assignment requires tracking the evolving matching state because each selected order-driver pair changes the remaining feasible candidates. GenMatch addresses these challenges with a Context-Aware Bipartite Encoder, a Business-Aware Utility Learner, and a State-Aware Pointer Decoder. Extensive offline evaluations and online A/B tests in five cities across DiDi's international ride-hailing markets show consistent improvements over competitive baselines, confirming the effectiveness and practicality of GenMatch for industrial order-dispatching.

发表机构

• Didi Chuxing（滴滴出行）
• The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））

机构由 AI 辅助整理，请以论文原文为准。