Multipurchase Assortment Optimization Under a General Random Utility Model

Choosing Product Assortments When Customers Buy More Than One Item

Retailers often need to decide which products to offer, but many existing assortment-optimization models assume that each customer buys at most one item. This paper studies a more realistic setting in which customers may purchase multiple items from an offered assortment while also considering outside options such as competing retailers, alternative channels, or not buying. The authors develop a general random utility model for this setting and show how the optimization problem can be approximated by a tractable surrogate problem. The surrogate is motivated by a large-offering asymptotic regime, can be solved efficiently, and performs well in numerical experiments, even for moderate-sized problems. The paper also provides theoretical bounds on the approximation error and develops an estimation framework that remains valid when outside-option choices are not directly observed.