Semantic Signal-Assisted Inspection and Recovery Allocation in Reverse Logistics

arXiv:2609.02116v1 Announce Type: new Abstract: Reverse-logistics operators often decide how to inspect and route returned assets before their condition is fully observed, while full inspection consumes scarce labor. Semantic Signal-Assisted Decision Support converts return notes into a condition factor and a signal-quality score that guide inspection depth and recovery allocation under shared labor capacity. We evaluate the framework in three synthetic benchmark scenarios spanning information technology decommissioning, aircraft maintenance, and consumer-electronics returns. Across 30 paired simulation seeds, the keyword implementation improves net recovery value relative to a structured-feature comparator with noisy full inspection while reducing inspection cost in all three scenarios. A risk-blind comparator that skips inspection altogether still records higher value under the benchmark's purely economic objective. At matched inspection cost, score-guided targeting adds 53.9 thousand United States dollars per batch in the aircraft scenario but has little economic effect in the other two configurations; phrase and large language model extractors provide further gains in the aircraft scenario. These results show how narrative evidence can support inspection allocation before recovery decisions are made.

Related

• AlphaTransit: Learning to Design City-scale Transit Routes
• Fixed-Budget Gaussian Volume Encoding with Structure-Aware Allocation
• The Traveling Thief Problem with Time Windows: Benchmarks and Heuristics
• Second-Order Policy Effects as State Transitions: A Source-Linked Benchmark for Policy Simulation

Source: arXiv cs.AI | 2026-09-03