Assortment management under the generalized attraction model with a capacity constraint

We consider an assortment optimization problem where a retailer chooses a set of substitutable products to maximize the total expected revenue or profit subject to a capacity constraint. The customer purchase behavior follows the generalized attraction model (GAM), of which the multinomial logit model and the independent demand model are special cases. We visualize the efficient assortments and propose a nonrecursive polynomial-time algorithm to find the optimal one for the static problem. We then connect it with the concept of efficient set in the context of revenue management and show that the efficient sets, the only candidates for the optimal assortments under the capacitated GAM formulation, are of polynomial size. Moreover, the efficient sets significantly reduce the computational complexity in the dynamic assortment management and the optimal policy has a time-threshold structure.