Abstract
We study assortment problems under the Marginal Exponential Model (MEM), which is an extension of the multinomial logit (MNL) model that can capture heteroscedasticity in the data. We first show that when the variance of the outside option is largest, one of the profit-nested assortment is optimal. This result generalized the well-known result for the MNL assortment optimization problem. Next, we show that the product assortment problem under MEM is NP-hard, but the best profit-nested assortment provides a good approximation to the optimal assortment. Furthermore, we improve existing MEM parameter estimation methods. Our numerical studies show that using MEM to capture choice behavior in assortment optimization leads to competitive results compared to other choice models that are also designed to capture heteroscedasticity.
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