Sustainable supply chain management using Bi-GRU network optimized by hybrid maritime search and rescue algorithm

The agricultural and food supply chain faces various challenges that include waste reduction, traceability, and profit optimization, with one being dynamic disturbances such as the weather events and transportation delays. As traditional methods have constraints in addressing these challenges due to a lack of adaptability and scalability, this study proposes a hybrid framework to resolve the problem. The study combines the Bidirectional Gated Recurrent Unit (Bi-GRU) networks and a Hybrid version of Maritime Search and Rescue (HMSR) for sustainable supply chain management. The production and storage prediction problem is handled using IoT sensors and market trends-based time series data by the Bi-GRU model, and the HMSR algorithm is used to optimize the decisions based on constraints, which can include production capacity, storage constraints, and market demand. Applied to the Global Food and Agriculture Statistics dataset on Kaggle and synthetic disruption scenarios, our framework reaches 92.4% accuracy in making storage decisions, reduces waste by 34.2% and maximizes profit margins at 28.7%, outperforming GRU, LSTM, GA, PSO, and HGSODL-ASCM baselines. These discoveries highlight that hybrid AI-driven artificial intelligence approaches can revolutionize agricultural structures and food supply chains, providing adaptable and scalable solutions for institutions in their pursuit of profitability and sustainability.