AI 中文总结
该研究提出语义信号辅助决策支持框架，将退货单转化为状态因子与信号质量评分，在三类逆向物流场景中验证其可提升净回收价值、降低检查成本，为回收决策前的检查分配提供支撑。
AI 中文摘要
逆向物流运营方通常需在退货资产状态未完全观测前，决定如何检查与调度，而全面检查会消耗稀缺人力。语义信号辅助决策支持（Semantic Signal-Assisted Decision Support）将退货单转化为状态因子与信号质量评分，在人力容量约束下指导检查深度与回收分配。我们在三个合成基准场景中评估该框架，涵盖信息技术退役、飞机维修及消费电子退货。在30组配对仿真种子中，与带噪声全面检查的结构化特征对比器相比，关键词实现方案在所有三个场景中均提升了净回收价值并降低了检查成本；完全跳过检查的风险盲对比器在基准纯经济目标下仍记录更高价值。在匹配的检查成本下，评分导向的靶向策略在飞机场景中每批次增加5.39万美元，而在其他两种场景中经济影响极小；短语与大语言模型（large language model）提取器在飞机场景中提供进一步增益。这些结果表明，叙事证据可在回收决策前为检查分配提供支持。
英文摘要
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.
CommentsAccepted at the IEEE 4th International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings 2026). 7 pages, 1 figure, 3 tables. Code and benchmark: https://github.com/jiani19980225/ssads-reverse-logistics
