Consumers prefer different brands of products, and stores also prefer certain brands for their high prot. By utilizing consumers' different familiarity and preferences over different brands, stores can generate more revenue by including high-profit brands along with the consumers' familiar products. Our work proposes such an assortment optimization Problem with different brands of products. We analyze the structure of the optional assortment and find that when the utilities of unfamiliar products are ordered, offering revenue-ordered sets for the unfamiliar products will be optimal. However, in general, revenue-ordered assortments would not be optimal, and we introduce an example to illustrate that. We then derive an approximation algorithm to solve the problem and the algorithm can re...[ Read more ]

Consumers prefer different brands of products, and stores also prefer certain brands for their high prot. By utilizing consumers' different familiarity and preferences over different brands, stores can generate more revenue by including high-profit brands along with the consumers' familiar products. Our work proposes such an assortment optimization Problem with different brands of products. We analyze the structure of the optional assortment and find that when the utilities of unfamiliar products are ordered, offering revenue-ordered sets for the unfamiliar products will be optimal. However, in general, revenue-ordered assortments would not be optimal, and we introduce an example to illustrate that. We then derive an approximation algorithm to solve the problem and the algorithm can reach a certain approximation ratio, Finally, we derive results for existing models in localized MNL model and endogenized MNL model. We show that revenue-ordered assortments can be useful under certain conditions.