Continuous Assortment Optimization with Logit Choice Probabilities and Incomplete Information
研究了产品种类连续而非有限的品类优化问题，针对未知模型参数设计数据驱动策略，并证明其遗憾上界和下界，表明策略渐近最优。
This paper considers a novel formulation of the classical assortment optimization problem with multinomial logit demand and unknown model parameters. The novelty lies in the fact that the set of products is not finite but a continuum, motivated by the desire to understand the problem characteristics for many products, as well as by applications where products are characterized by a continuous quality variable. For settings with and without capacity constraints, the authors design data-driven decision policies and prove upper and lower bounds on the regret, which imply that these policies are asymptotically optimal.