用返修描述生成条件因子，优化逆向物流的质检与回收决策。
Semantic Signal-Assisted Inspection and Recovery Allocation in Reverse Logistics
- 将退货说明转为条件因子与信号质量分，指导质检深度与回收分配。
- 飞机维护场景下，成本相同时每批次提升53.9万美元净回收价值。
- 适合资源有限、依赖文本信息做决策的逆向物流系统应用。
逆向物流运营方常需在未完全观测资产状况时决定质检与路由，而全面质检消耗稀缺人力。语义信号辅助决策支持将退货说明转化为条件因子与信号质量评分，指导在共享人力条件下进行质检深度与回收分配。我们在三个合成基准场景中评估该框架：信息技术设备退役、飞机维修及消费电子退货。在30组配对仿真种子下，关键词实现方案相比含噪声全检的结构特征对比器，在三类场景中均提升了净回收价值并降低质检成本。一个忽略风险、跳过质检的对比器在纯经济目标下仍表现更优。在匹配质检成本时，评分引导的精准投放在飞机场景中每批次增加53.9千美元收益，而在另两个配置中影响较小；短语与大语言模型提取器在飞机场景中带来进一步增益。结果表明，叙述性证据可在回收决策前有效支持质检资源配置。
原文摘要 · Abstract (English)
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.
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。