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We propose a multi-stage choice model in which customers can choose multiple products and multiple units of each product in a single shopping trip. We characterize the optimal assortment under the cardinality, space, and basket size constraints, respectively. We prove the NP-hardness of the problem under the latter two constraints and develop approximation algorithms to find near optimal assortments. We solve the price optimization problem efficiently and provide a calibration method.

This paper studies assortment optimization and product line design problems under the mixed multinomial logit model and discrete pricing. Both literature streams are connected by improving exact, extending approximate, and novel heuristic methods. We show that an FPTAS algorithm exists even if prices are taken into account. To improve the state-of-the-art conic formulation, valid inequalities are introduced to a branch and cut method. Our results show an average time reduction of 35 % - 66 %.

Many product recalls are caused by quality-related product failures. This paper analyzes quality and pricing strategies for competing firms facing the risk of a severe quality-related recall making the product hazardous and leading to its removal from the market. We develop a two-stage Nash game where the probability of recall depends on the firms’ chosen quality investments, and either firm can experience a recall.

Customers are concerned about personalized pricing (PP) tactics. Using a Bayesian persuasion framework, we study whether and under what conditions price can signal such PP implementation to customers. We also investigate whether disclosing inventory availability information can alleviate customer concerns and benefit the firm and customers. We show that price alone may not signal PP, and firms can create transparency over the pricing strategies by disclosing inventory availability information.