Optimizing an Omnichannel Retail Strategy Considering Customer Segmentation.

Unlike previous studies on fixed logistics nodes, this research explored how consumer distribution impacts store selection and inventory balance, integrating the ship-from-store strategy to increase fulfillment within multiperiod sales plans. Specifically, omnichannel retailers (O-tailer) must sequentially decide on inventory replenishment from suppliers to the distribution center (DC), allocation from the DC to stores, and which department will fulfill online orders. We introduce a multiperiod stochastic optimization model and solve it with a robust two-stage approach (RTA). In Stage 1, we use the K-means algorithm and silhouette coefficients to determine the optimal number of stores. In Stage 2, linear decision rule (LDR) are employed to decide on replenishment, allocation, and order fulfillment quantities. Numerical experiments show that RTA outperforms existing methods, achieving solutions with efficiency gaps of less than 10%, even when assumptions are not fully met. Additionally, the sensitivity analysis shows that variations in product prices, fulfillment costs, market share, and customer distribution consistently lead to greater profits with the ship-from-store strategy.