Latency-Aware Bid Acceptance under Operational Feasibility: A Public Benchmark with Hindsight Ceilings

Jul 16, 2026

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Online truckload bid acceptance is modeled as a closed-loop stochastic decision problem where carriers or brokers must accept or reject tenders in real time under operational feasibility, fleet repositioning costs, and opportunity cost from future demand. FreightBidBench is introduced as a public, calibrated, dependency-free benchmark with explicit, versioned feasibility and economic components derived from public Freight Analysis Framework and USDA truck-rate data. It formalizes a closed-loop accept/reject MDP, adds hindsight diagnostics and tight ceiling bounds, and evaluates a parametric surrogate rollout cascade with escalation triggers.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

Abstract

Introduction

Problem statement and constraints

Benchmark gaps in existing routing work

FreightBidBench v0.2 background

arXiv :2607 .07343v 1 [ cs .LG] 8 Jul 2026 Latency-Aware Bid Acceptance under Operational Feasibility: A Public Benchmark with Hindsight Ceilings ∗ Aswin Chandrasekaran Bubba AI [aswin@bubba. ai](aswin@bubba. ai) July 2026 Abstract 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. Building on this benchmark, we make three contributions. First, we formalize the v0 .3 closed-loop accept/reject MDP and show that the three new reward components each isolate a distinct policy class: a service-failure penalty creates linear regret for feasibility-blind policies, terminal fleet value penalizes greedy positioning, and a temporal price-premium window penalizes future-blind timing. Second, we develop three complementary hindsight diagnostics: an exact realized-seed dynamic program for small load prefixes, a simple LP-style full-horizon upper bound, and a Lagrangian-per-truck information-relaxation bound that retains per-truck HOS and sequencing structure and is 20 . 7% tighter than the LP relaxation on tight and 39 .3% tighter on scarce while remaining dependency-free, justified as an information relaxation in the sense of Brown et al. [2010] . Third, we introduce a parametric surrogate-rollout cascade with two escalation triggers — a boundary band β ≥ 0 on the surrogate’s signed score and a scarcity-pressure threshold κ on the count of immediately available trucks in the origin market—characterize its limit behaviour (rollout-call share monotone in either trigger threshold), and show that the scarcity trigger alone captures the high-stakes capacity decisions on which the surrogate is least reliable. On ten-seed tight and scarce scenarios, the best simple policy retains 91.0 percent and 86.5 percent of rollout profit, respectively, and a stdlib linear surrogate 94.2 percent and 89.3 percent; a cascade at a single escalation band recovers roughly 98 percent of rollout value on both at 40–56 percent of rollout’s mean decision latency, and on tight is statistically indistinguishable from the rollout teacher (paired-bootstrap 95% CI on the profit delta spans zero) . The release contains a versioned manifest, layer ablations, sensitivity sweeps, and an exact-plus-relaxed ceiling, providing a reproducible test bed for future methods work. ∗ Code and benchmark artifacts: [https://github.com/aswincsekar/freightbidbench](https://github.com/aswincsekar/freightbidbench. Artifact)[. Artifact](https://github.com/aswincsekar/freightbidbench. Artifact) release: freightbidbench-v0.3, scenario contract scenario-v0.3.2, policy set policy-set-v0 .3 .0. 1 Introduction Truckload carriers and brokers face an online accept/reject decision each time a load tender arrives. The decision is constrained by latency: tenders in a typical broker workflow must be priced and accepted or rejected within seconds. The decision is operationally constrained: a load can only be served by a truck that can reach the pickup, satisfies appointment windows and hours-of-service (HOS) clocks, and remains feasible through delivery. The decision is economically constrained: a tender with positive immediate margin can still be a poor decision if it consumes a scarce t

FAQs

What is the core decision problem addressed by the document?

It models online truckload bid acceptance as a closed-loop stochastic accept/reject decision that must account for operational feasibility, fleet repositioning costs, and opportunity costs from future demand.

What is FreightBidBench and what makes it different from prior benchmarks?

FreightBidBench is a public-calibrated, dependency-free, reproducible closed-loop benchmark where feasibility and economics are explicit, versioned, and derived from public datasets, unlike static routing benchmarks or private-data dynamic studies.

How do the document’s “hindsight ceilings” help evaluate policies?

They provide complementary hindsight diagnostics—exact realized-seed dynamic programming, a full-horizon upper bound, and an information-relaxation bound—so policy evaluation is anchored to measured ceilings rather than only finite rollout teachers.

Jul 16, 2026

Upload by Levi