Assortment Optimization: An Annotated Reading Assortment

Which varieties or brands of a product should a retailer stock on its shelf? Carrying a large variety caters to more customers' needs, but could cannibalize the sales of high-end brands and also cause an inventory nightmare. Assortment optimization aims to formalize these tradeoffs, with the basic problem being as follows. There is a universe of brands j is an element of U , each with a market-accepted price r(j) . For any S subset of U , a function phi(j, S) indicates the probability that a representative customer from the population would purchase j when given the choice from assortment S , under the market prices. The optimization problem is to maximize the average revenue per customer, i.e max(S)X Sigma(j is an element of S) r(j)phi(j,S), (1) possibly with constraints on S due to shelf size. Assortment optimization started out by showing how to efficiently find the optimal S from the exponentially many possibilities, under well- established parametric forms for the function phi that are called (discrete) choice models. Since then, the literature has developed choice models of its own that are specialized for assortment optimization. The basic problem has also been extended, and connected with topics such as online algorithms, machine learning, and mechanism design that are mainstream in the Economics and Computation community, with a vast horizon for future directions. This is an annotated reading list about assortment optimization, that aims to provide broad coverage while facing a "cardinality constraint" on the number of papers in the assortment.