提出公开可复现的货运投标决策基准，提升实时投标效率与收益。
Latency-Aware Bid Acceptance under Operational Feasibility: A Public Benchmark with Hindsight Ceilings
- 构建基于真实数据的闭环投标基准，明确运营可行性与经济约束。
- 设计两种后见之明上界，其中拉格朗日松弛法比线性规划紧20.7%~39.3%。
- 提出分层代理滚动策略，在低延迟下恢复近98%收益，适合实际系统部署。
在线整车投标是一个闭环随机决策问题，承运商或货主需在实时条件下判断是否接受装载任务，同时考虑运营可行性、车队调度成本及未来需求的机会成本。现有基准多为静态或依赖私有数据，缺乏公开可复现性。本文提出FreightBidBench，一个公开校准、无依赖、闭环的基准，显式建模可行性（取货可达性、预约窗口、简化小时服务规则、随机场站延误）与经济因素（服务失败惩罚、终端车队价值、每日溢价窗口），所有参数均来自公开的交通分析框架与美国农业部卡车费率数据。我们构建两个全时域后见之明上界：一种为简单线性规划松弛，另一种为保留每车小时服务与顺序结构的拉格朗日信息松弛，其在紧容量场景下比线性规划紧20.7%，在稀缺容量场景下紧39.3%。引入带边界带和稀缺压力触发的参数化代理滚动级联策略。在十组种子的紧与稀缺场景中，最优简单策略分别保留滚动上界91.0%与86.5%利润，标准库代理保留94.2%与89.3%；单级联触发器在40-56%平均决策延迟下恢复约98%收益，且在紧场景中与滚动教师策略无统计差异（配对bootstrap 95%置信区间包含零）。
原文摘要 · Abstract (English)
Online truckload bid acceptance is a closed-loop stochastic decision problem in which a carrier or broker must, in real time, accept or reject a tendered load subject to operational feasibility, fleet repositioning costs, and opportunity cost against future demand. Public, reproducible benchmarks for this problem are scarce: existing routing benchmarks are static, while dynamic-fleet studies typically rely on private operator data. We introduce FreightBidBench, a public-calibrated, dependency-free, closed-loop benchmark in which feasibility (pickup reach, appointment windows, simplified hours-of-service, stochastic yard delays) and economics (service-failure penalty, terminal fleet value, daily price-premium window) are explicit, versioned, and reproducible from public Freight Analysis Framework and U.S. Department of Agriculture truck rate data. We develop two full-horizon hindsight ceilings: a simple LP style relaxation and a tighter Lagrangian-per-truck information relaxation that retains per-truck hours-of-service and sequencing structure and is 20.7% tighter than the LP relaxation on a tight-capacity scenario and 39.3% tighter on a scarce-capacity scenario. We introduce a parametric surrogate-rollout cascade with boundary-band and scarcity-pressure escalation triggers. On ten-seed tight and scarce scenarios, the best simple policy retains 91.0% and 86.5% of rollout profit and the standard-library surrogate 94.2% and 89.3%; a cascade at a single escalation band recovers roughly 98% on both at 40-56% of rollout's mean decision latency, and on the tight scenario is statistically indistinguishable from the rollout teacher (paired-bootstrap 95% CI on the profit delta spans zero).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。