Semantic Signal-Assisted Inspection and Recovery Allocation in Reverse Logistics

2026-09-02

AI Summary

研究通过将退货说明转化为条件因素和信号质量分数来指导反向物流中的检查深度和恢复分配，以减少劳动力消耗并提高净回收价值。

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.

Problem

• reverse logistics
• inspection
• labor capacity
• recovery allocation
• condition factor

Innovation

• Semantic Signal-Assisted Decision Support
• condition factor
• signal-quality score
• shared labor capacity
• inspection depth