A0841
Title: PASTA: Pessimistic assortment optimization
Authors: Zhengling Qi - The George Washington University (United States) [presenting]
Abstract: A class of assortment optimization problems is considered in an offline data-driven setting. A firm does not know the underlying customer choice model but has access to an offline dataset consisting of the historically offered assortment set, customer choice, and revenue. The objective is to use the offline dataset to find an optimal assortment. Due to the combinatorial nature of assortment optimization, the problem of insufficient data coverage is likely to occur in the offline dataset. Therefore, designing a provably efficient offline learning algorithm becomes a significant challenge. To this end, an algorithm is proposed referred to as Pessimistic ASsortment opTimizAtion (PASTA for short), designed based on the principle of pessimism, that can correctly identify the optimal assortment by only requiring the offline data to cover the optimal assortment under general settings. In particular, a regret bound is established for the offline assortment optimization problem under the celebrated multinomial logit model. An efficient computational procedure is also proposed to solve the pessimistic assortment optimization problem. Numerical studies demonstrate the superiority of the proposed method over the existing baseline method.
