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Online product pricing is an important factor that affects the purchase of customers and the earnings of e‐commerce platform enterprises. It has an increasingly prominent influence on consumers' purchasing decisions. To study the influence of customers' anchoring psychology and product pricing on customers' purchasing behaviors, this paper proposes a pricing model that considers online consumers to be anchored by the product price and sales. The cognitive bias of online consumers is described by a utility function based on an anchoring‐adjustment heuristic, and consumer choice behavior and an online product pricing strategy are studied with a multinomial logit model. Then, it studies the influence of the anchoring point and anchoring degree on optimal pricing, sales and profit. In particular, when consumers are anchored only by sales of goods, the optimal solution is monotonic with respect to the anchoring degree. However, when consumers are anchored by both price and sales, both factors affect consumer behaviors and produce more complex results. This paper provides a flexible pricing mechanism for platform enterprises, and it can provide a theoretical basis and decision support for dynamic pricing of platform enterprises based on historical consumption data. [ABSTRACT FROM AUTHOR]

Incorporating consumer choice behavior into a product line design optimization model enhances the understanding of consumer choices and improves the opportunities to increase profit. Most product line optimization problems assume that parameters are precisely known in consumer choice model. However, the decision maker does not precisely know the model parameters because of insufficient sample data, measurement problems, and other factors. We investigate the problem of establishing robust product line pricing under a multinomial logit model to account for the uncertainty of the valuation parameter. First, we present a nominal product line model to maximize profit. We then establish a robust product line model to maximize the worst-case expected profit, where the valuation parameter lies in an uncertainty set. We consider both single and multiple products development and derive the optimal prices' closed-form expressions. Through numerical experiments, we illustrate the benefit of robust product line pricing to address parameter uncertainty. We demonstrate that the difference between the expected nominal profit and the worst-case profit increases with the increase of the interval of the uncertainty set, and the robust profit relative to the worst-case nominal profit improves. The robust product line design can ensure steadier, even higher profit. [ABSTRACT FROM AUTHOR]