Agentic AI for Cold-Chain Sustainability: LSTM-Based Predictive Logistics
Executive Summary:
This repository implements an Agentic AI framework designed to revolutionize cold-chain logistics by shifting from reactive monitoring to autonomous intervention. By integrating Ambient IoT data with LSTM (Long Short-Term Memory) neural networks, the system predicts thermal breaches and executes rerouting logic to mitigate perishable goods spoilage.
How It Works: The Sense-Think-Act Cycle
The simulation above demonstrates the framework's ability to intervene before biological degradation becomes irreversible:
-
Sense: Continuous ingestion of high-frequency telemetry (Temp, RH,
$C_2H_4$ ) via LoRaWAN/5G edge nodes. -
Think: An LSTM Neural Network forecasts the internal cargo temperature with a 3.5-hour lead time. Simultaneously, the system calculates the Kinetic Risk using the Arrhenius Equation:
$$k = Ae^{-\frac{E_a}{RT}}$$ -
Act: If a breach is predicted, the Agent autonomously identifies the nearest Micro-Fulfillment Center (MFC) and updates the GPS destination via logistics APIs—bypassing manual decision latencies.
Key Performance Benchmarks:
- 66% Reduction in post-harvest spoilage compared to traditional FIFO (First-In, First-Out) models.
- 3.5-Hour Predictive Lead Time for thermal excursion warnings.
- 1.2-Second Decision Latency for autonomous rerouting protocols.
- Sustainability Impact: Significant reduction in carbon footprint via optimized cooling and waste mitigation.
Core Methodology:
The framework operates on a four-tier architecture:
- Sensing Layer: Continuous data ingestion from Ambient IoT sensors (Temperature, Humidity,
$CO_2$ ). - Perception Layer: An LSTM neural network analyzes temporal patterns to forecast internal cargo temperatures 4 hours into the future.
- Reasoning Layer (Kinetic Logic): The system applies the Arrhenius Equation to calculate the real-time chemical degradation of the cargo, moving beyond static temperature thresholds.
- Action Layer (Agency): If a breach is predicted, the AI autonomously initiates Dynamic FEFO (First-Expired, First-Out) rerouting, interacting with logistics ERPs to secure the inventory at the nearest fulfillment center.
Technology Stack:
- Language: Python 3.10+
- Deep Learning: TensorFlow / Keras (LSTM Architecture)
- Data Processing: Pandas, NumPy, Scikit-learn
- IoT Simulation: MQTT protocol / Ambient IoT data streams
- Deployment: Optimized for Edge Computing environments
Research Context:
This code is the official implementation of the research published in the World Journal of Advanced Research and Reviews (WJARR): Vasanthakumar Padmanaban. From Farm to Fork: Optimizing Cold-Chain Logistics through IoT and Machine Learning. World Journal of Advanced Research and Reviews, 2026, 29(02), 671-677. Article DOI: https://doi.org/10.30574/wjarr.2026.29.2.0350.
Getting Started:
- Clone the Repo: git clone https://github.com/vasanthpresearch/agentic-ai-cold-chain-optimization.git
- Install Dependencies: pip install -r requirements.txt
- Run Simulation: Execute main.py to view the predictive logic and rerouting triggers.
Collaboration & Citations:
I am an Independent Researcher focused on AI-driven infrastructure and sustainable logistics. If you use this framework in your research, please cite the WJARR paper (Vasanthakumar Padmanaban. From Farm to Fork: Optimizing Cold-Chain Logistics through IoT and Machine Learning. World Journal of Advanced Research and Reviews, 2026, 29(02), 671-677. Article DOI: https://doi.org/10.30574/wjarr.2026.29.2.0350.). For collaboration inquiries, feel free to reach out via LinkedIn or GitHub issues.