Assortment optimization describes a retailer's general problem of deciding which variants in a product category to offer. In a typical formulation, there is a universe of substitute products whose prices have been pre-determined, and a model for how customers choose between these products. The goal is to find a subset to offer that maximizes aggregate revenue. In this paper we ask whether offering an assortment is actually optimal, given the recent emergence of more sophisticated selling practices, such as offering certain products only through lotteries. To formalize this question, we introduce a mechanism design problem where the items have fixed prices and the seller optimizes over (randomized) allocations. The seller has a Bayesian prior on the buyer's ranking of the items along with an outside option. Under our formulation, revenue maximization over deterministic mechanisms is equivalent to assortment optimization, while randomized mechanisms allow for lotteries that sell fixed-price items. We derive a sufficient condition, based purely on the buyer's ranking distribution, that guarantees assortments to be optimal within this larger class of randomized mechanisms. Our sufficient condition captures many preference distributions commonly studied in the assortment optimization literature -- Multi-Nomial Logit (MNL), Markov Chain, Tversky's Elimination by Aspects model, a mixture of MNL with an Independent Demand model, and simple cases of Nested Logit. When our condition does not hold, we also bound the suboptimality of assortments in comparison to lotteries. Finally, from these results emerge two findings of independent interest: an example showing that Nested Logit is not captured by Markov Chain choice models, and a tighter Linear Programming relaxation for assortment optimization.
翻译:批量优化描述零售商的一般问题, 即决定产品类别中要提供哪种变种。 在典型的配方中, 存在一系列价格预先确定的替代产品, 以及客户选择这些产品的模式。 目标是找到一个子集来提供最大总收入。 在本文中, 我们问提供批量是否真正是最佳的, 鉴于最近出现了更先进的销售做法, 比如只通过彩票提供某些产品。 为了正式解决这个问题, 我们引入了一个机制设计问题, 即产品已经固定价格, 卖方优化了( 随机化) 分配。 在典型的配方中, 存在一系列替代产品, 价格预先确定, 以及客户选择这些产品的模式。 在我们的配方中, 确定性机制的收益最大化相当于排序优化, 而随机化机制允许销售固定价格物品的彩色。 我们从买方的排名分布中得出一个充分的条件, 保证独立变价的变价在更大的两类随机化模式中达到最佳的( 随机化) 。 我们的变价变价还显示许多的变价结果, 我们的变价的比, 将显示许多变价的变价, 以正的变价法, 我们的变价的变价法作为正法的缩的模型中通常的排序作为中, 我们的变价的变价法, 的变价的变法作为一个普通的变价的变法, 。