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Assortment Optimization Under the Mallows model
Antoine Desir · Vineet Goyal · Srikanth Jagabathula · Danny Segev

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #121 #None

We consider the assortment optimization problem when customer preferences follow a mixture of Mallows distributions. The assortment optimization problem focuses on determining the revenue/profit maximizing subset of products from a large universe of products; it is an important decision that is commonly faced by retailers in determining what to offer their customers. There are two key challenges: (a) the Mallows distribution lacks a closed-form expression (and requires summing an exponential number of terms) to compute the choice probability and, hence, the expected revenue/profit per customer; and (b) finding the best subset may require an exhaustive search. Our key contributions are an efficiently computable closed-form expression for the choice probability under the Mallows model and a compact mixed integer linear program (MIP) formulation for the assortment problem.

Author Information

Antoine Desir (Columbia University)
Vineet Goyal (Columbia University)
Srikanth Jagabathula (NYU Stern School of Business)
Danny Segev (University of Haifa)

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