A robust omnichannel pricing and ordering optimization approach with return policies based on data-driven support vector clustering

This work considers an omnichannel retailer selling a product with market demand uncertainties to customers in different regions through an e-store and multiple brick-and-mortar stores. The retailer also manages online product returns by selecting appropriate return policies. A robust omnichannel pricing and ordering optimization model is proposed with two, i.e., a full-refund and a no-refund, return policies. The online demand and the offline demand depend on the prices of the e-store and the brick-and-mortar stores, where the online demand also depends on the refund of the e-store. A linearization technique is adopted to deal with nonlinearity of the model. A data-driven robust optimization approach is used to construct uncertainty sets based on available historical data using support vector clustering to handle demand uncertainties. Furthermore, the proposed model is transformed into an approximate mixed integer linear programming model which can be solved by using commercial software. An electronics retailer in China is used as a case study to illustrate the effectiveness and practicality of the proposed model and the solution method. A comparison with the box uncertainty set reveals that the data-driven uncertainty set is less conservative and performs better by obtaining higher profit for the retailer. Furthermore, sensitive analysis results indicate that return policies and the return rate of the product affect the optimal pricing and ordering decisions and the total profit.