Optimizing shipping-fee schedules to maximize e-tailer profits

Shipping-fee charges by online retailers have been known to impact customers' order incidence and cart size. While the importance of managing shipping fees is well documented, few studies have provided normative guidelines for e-tailers to determine optimal shipping-fee schedules. This paper provides two nonlinear mixed-integer programming models to optimize e-tailers' shipping-fee charges for single and multiple product transactions. Given e-tailers' cost information and heterogeneity across consumers' reservation prices and delivery time requirements, our models aim to concurrently determine the optimal shipping-fee schedules and product selling prices. To solve the sophisticated models in real-time, we develop search techniques based on the concept of genetic algorithms. Numerical studies indicate that the proposed methods offer attractive product prices and low shipping charges. The proposed models not only meet the online requirement of instant response time, but also draw more customers and enhance e-tailer profitability.