Optimization of Inventory Routing and Pricing Problem for Omnichannel E-commerce

This paper studied the inventory routing and pricing problem for e-commerce companies operating in the omnichannel mode. Considering the uncertain demand factor of each front warehouse, a differentiated pricing strategy for goods in different selling channels was proposed. A mixed-integer nonlinear robust optimization model was constructed with the objective of maximizing the total profit. The e-commerce company's risk attitude on the demand uncertainty is set by the conservative coefficient. And then an adaptive simulated annealing particle swarm algorithm was designed to solve it. Two sets of examples were selected, including 10 and 20 front warehouses, to verify the applicability and effectiveness of the proposed model and algorithm. The results of experimental analyses show that differentiated pricing can increase the total profit of the e-commerce company by about 5% and 6%, respectively, compared with uniform pricing. The results of the sensitivity analysis indicate that, enhancing offline shopping experiences of online customers to increase the number of customers who buy online and pick-up in store, and organizing online marketing activities to increase the sensitivity of online customers to e-commerce promotion efforts, can bring higher profits to e-commerce companies. Controlling future market volatility risks and accurately predicting demand information to reduce e-commerce companies' conservative coefficients and maximum demand deviation coefficients can also increase total profits of e-commerce companies. The findings of the study can provide a reference for e-commerce companies to formulate inventory routing strategies for their front warehouses and goods pricing schemes for their selling channels. © 2024 Science Press. All rights reserved.