A Conic Integer Programming Approach to Constrained Assortment Optimization under the Mixed Multinomial Logit Model
  We consider the constrained assortment optimization problem under the mixed
multinomial logit model. Even moderately sized instances of this problem are
challenging to solve directly using standard mixed-integer linear optimization
formulations. This has motivated recent research exploring customized
optimization strategies and approximation techniques. In contrast, we develop a
novel conic quadratic mixed-integer formulation. This new formulation, together
with McCormick inequalities exploiting the capacity constraints, enables the
solution of large instances using commercial optimization software.
