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

Abstract

Problem definition: We study a novel practice in the urban logistics sector, the transshipment-based urban courier (TUC) system. To achieve a better trade-off between transportation costs and delivery efficiency, large-capacity vehicles are utilized in the TUC system as moving warehouses to consolidate packages from individual couriers flexibly. In this paper, we focus on the joint scheduling and pricing problem, which jointly optimizes the coverage schedule of the mainline large-capacity vehicles and local pricing decision that affects demand. The mainline scheduling and local pricing decisions are two of the most critical decision in a TUC system because the performance of the TUC system depends significantly on the coordination of the transportation services provided by the system and the prices it charges customers. Methodology/Results: We model the joint scheduling and pricing problem as a coverage-and-pricing model, where the transportation schedule covers the price-adjusted local demand. To address the uncertainty in the system, we develop a distributionally robust method to estimate the local demand function from historical data. The data-driven robust method is embedded into our coverage-and-pricing model, which entails tractable and theoretically robust solutions. The coverage-and-pricing model can be solved by a decomposition-based heuristic, which satisfies worst-case performance guarantees under regularity conditions. The decomposed coverage-and-pricing model 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. Managerial implications: The TUC system is an innovation in urban logistics with a better trade-off between delivery speed and transportation costs. Our study investigates operational decisions of transportation services and pricing in a TUC system and highlights the importance of jointly optimizing these decisions. We also provide efficient methods for achieving this goal with our coverage-and-pricing model.

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