Integrating Attention-Enhanced LSTM and Particle Swarm Optimization for Dynamic Pricing and Replenishment Strategies in Fresh Food Supermarkets
2025-09-17
Summary
The article discusses a new approach for optimizing pricing and replenishment in fresh food supermarkets using a combination of Attention-Enhanced Long Short-Term Memory (LSTM) networks and Particle Swarm Optimization (PSO). This model forecasts sales, pricing trends, and spoilage rates to inform dynamic pricing and inventory decisions, aiming to maximize profitability and reduce food waste. The framework was tested in a supermarket in Lagos, Nigeria, demonstrating its potential to enhance decision-making in multicultural retail environments.
Why This Matters
This research is significant as it provides a scalable, data-driven solution for supermarkets, particularly in regions like Africa where managing perishable goods is a complex challenge. By improving profit margins and reducing food waste, the approach supports sustainable retail practices and addresses broader issues like food security and operational resilience. The integration of machine learning with operational optimization offers a modern tool for businesses to adapt to market fluctuations effectively.
How You Can Use This Info
Professionals in retail management can use these insights to implement more adaptive pricing and replenishment strategies, thereby improving operational efficiency. This framework can be adapted to other sectors dealing with perishables, such as pharmaceuticals or floriculture, where dynamic strategies are crucial. By leveraging this technology, businesses can transition from reactive to proactive management processes, enhancing both profitability and sustainability.