Assortment Optimization

Assortment optimization focuses on selecting the best subset of products to offer customers, maximizing revenue or profit. Current research emphasizes developing efficient algorithms and models, such as those based on multinomial logit (MNL) choice models, generative adversarial networks (GANs), and contextual bandit approaches, to handle the complexities of large product catalogs and diverse customer preferences. These advancements are improving real-time personalization in e-commerce and enhancing inventory management in both online and brick-and-mortar retail, leading to more effective and data-driven decision-making in various industries. Furthermore, research is exploring the integration of machine learning techniques, such as graph embeddings and neural networks, to better understand customer behavior and improve assortment planning.