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We consider the constrained assortment optimization problem under the mixedmultinomial logit model. Even moderately sized instances of this problem arechallenging to solve directly using standard mixed-integer linear optimizationformulations. This has motivated recent research exploring customizedoptimization strategies and approximation techniques. In contrast, we develop anovel conic quadratic mixed-integer formulation. This new formulation, togetherwith McCormick inequalities exploiting the capacity constraints, enables thesolution of large instances using commercial optimization software.

A Conic Integer Programming Approach to Constrained Assortment Optimization under the Mixed Multinomial Logit Model