Dynamic pricing for ship-from-store omnichannel retailers: A globalized robust optimization approach

In order to provide consumers with seamless shopping experiences, retailers integrate different sales channels to form omnichannel sales. We design a dynamic pricing model for an omnichannel retailer with ship-from-store (SFS) that manages an online channel and multiple physical stores, and consider the joint decision problems of dynamic pricing, inventory control, and online orders realization, so that the retailer can obtain the optimal price and inventory holding at different stages and maximize the profit. Since demand from different sales channels is uncertain in the omnichannel environment, we adopt the globalized robust optimization approach to deal with the uncertainty in the constraints due to the demand function and make the model computationally tractable through a series of transformations. Then, numerical experiments are conducted on the constructed model using the filter algorithm based on the trust region method. The experimental results demonstrat the correctness and effectiveness of the model. Compared with the deterministic model, the results of the robust optimization model are more secure and can effectively cope with uncertainty. Furthermore, we conducted sensitivity analyses and supplementary experiments. Based on the empirical findings, we put forward reasonable management suggestions for omnichannel retailers according to the experimental results. For example, with the increase in channel consumers, prices can be appropriately raised to obtain higher profits.