Urban Courier: Operational Innovation and Data-driven Coverage-and-Pricing

Abstract

We study a novel practice in the urban logistics sector, the transshipment-based urban courier (TUC) system. In the TUC system, large-capacity vehicles are utilized as moving warehouses to flexibly consolidate packages from individual couriers. The goal is to achieve a better trade-off between transportation costs and delivery efficiency. Because the performance of the TUC system depends significantly on the coordination of the transportation services and the pricing system for consumers, we consider the joint transportation coverage and pricing problem. In particular, we propose a data-driven robust pricing model in coordination with a transportation coverage model. Theoretical performance guarantees and numerical results verify the robustness of the pricing decision. The coverage-and-pricing model can be solved by a decomposition-based heuristic. This heuristic is a valid approximation algorithm under sufficient regularity conditions. After decomposition, the coverage-and-pricing problem can be reformulated as a maximum weighted coverage problem and solved via column generation, where the column-generating subproblems can be solved efficiently by a dynamic programming algorithm. Numerical experiments and a case study based on operational data from DML-Express demonstrate the efficiency of our method and provide managerial implications for the pricing policy in a TUC system.

Keywords: urban courier delivery, joint scheduling and pricing, data-driven pricing, distributionally robust optimization, maximum weighted coverage