用物联网数据动态调温，让生鲜物流保鲜期提升18%以上。
IoT-based Fresh Produce Supply Chain Under Uncertainty: An Adaptive Optimization Framework
- 基于物联网温度反馈，动态优化运输路径与温控策略。
- 相比传统方法，生鲜保质期延长超18%，显著减少损耗。
- 适合关注冷链物流优化与智能供应链的从业者。
果蔬是全球经济的重要组成部分，但其配送面临高易腐性、供应波动、严格质量和安全标准以及环境敏感等复杂挑战。本文提出一种自适应优化模型，考虑延误、行程时间及温度变化对农产品货架期的影响，并与鲁棒优化、分布鲁棒优化和随机规划等传统方法进行对比。利用物联网（IoT）传感器数据开展一系列计算实验，结果表明，所提自适应模型通过温度反馈机制动态缓解温差，使货架期较传统模型提升超过18%，显著改善了物流系统的鲜度与效率，弥补了以往研究中对此类问题的忽视。
原文摘要 · Abstract (English)
Fruits and vegetables form a vital component of the global economy; however, their distribution poses complex logistical challenges due to high perishability, supply fluctuations, strict quality and safety standards, and environmental sensitivity. In this paper, we propose an adaptive optimization model that accounts for delays, travel time, and associated temperature changes impacting produce shelf life, and compare it against traditional approaches such as Robust Optimization, Distributionally Robust Optimization, and Stochastic Programming. Additionally, we conduct a series of computational experiments using Internet of Things (IoT) sensor data to evaluate the performance of our proposed model. Our study demonstrates that the proposed adaptive model achieves a higher shelf life, extending it by over 18\% compared to traditional optimization models, by dynamically mitigating temperature deviations through a temperature feedback mechanism. The promising results demonstrate the potential of this approach to improve both the freshness and efficiency of logistics systems an aspect often neglected in previous works.
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