IoT-based Fresh Produce Supply Chain Under Uncertainty: An Adaptive Optimization Framework

TL;DR

This paper introduces an adaptive IoT-based optimization framework for fresh produce supply chains that dynamically manages temperature and delays, significantly extending shelf life and improving logistics efficiency.

Contribution

It presents a novel adaptive optimization model that incorporates IoT sensor data to better handle perishability and environmental factors in supply chain management.

Findings

Shelf life extended by over 18% using the proposed model.

Adaptive model outperforms traditional approaches like Robust Optimization.

Temperature feedback mechanism effectively mitigates spoilage risks.

Abstract

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.