This research uses machine learning, data mining, and optimization techniques to identify hidden relationships between products in retail shopping baskets. By analyzing over two million transactions, it predicts how promotions affect demand across products and helps retailers design smarter discount strategies, improve inventory planning, increase profits, and enhance customer satisfaction.

This research improves large-scale optimisation by combining problem decomposition with machine learning. By identifying similarities between subproblems, it predicts solutions instead of solving each independently, reducing computational cost. The approach enhances efficiency in logistics and extends to applications such as healthcare scheduling and transport network design.