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Pricing and Demand Management for Integrated Same-Day and Next-Day Delivery Systems

运筹学 计算机科学 分段 利润(经济学) 数学优化 经济 微观经济学 数学 数学分析
作者
Dipayan Banerjee,Alan L. Erera,Alejandro Toriello
出处
期刊:Transportation Science [Institute for Operations Research and the Management Sciences]
卷期号:59 (2): 279-300 被引量:3
标识
DOI:10.1287/trsc.2023.0381
摘要

We study a system in which a common delivery fleet provides service to both same-day delivery (SDD) and next-day delivery (NDD) orders placed by e-retail customers who are sensitive to delivery prices. We develop a model of the system and optimize with respect to two separate objectives. First, empirical research suggests that fulfilling e-retail orders ahead of promised delivery days increases a firm’s long-run market share. Motivated by this phenomenon, we optimize for customer satisfaction by maximizing the quantity of NDD orders fulfilled one day early given fixed prices. Next, we optimize for total profit; we optimize for a single SDD price, and we then set SDD prices in a two-level scheme with discounts for early-ordering customers. Our analysis relies on continuous approximation techniques to capture the interplay between NDD and SDD orders and particularly the effect one day’s operations have on the next, a novel modeling component not present in SDD-only models; a key technical result is establishing the model’s convergence to a steady state using dynamical systems theory. We derive structural insights and efficient algorithms for both objectives. In particular, we show that, under certain conditions, the total profit is a piecewise-convex function with polynomially many breakpoints that can be efficiently enumerated. In a case study set in metropolitan Denver, Colorado, approximately 10% of NDD orders can be fulfilled one day early at optimality, and profit is increased by 1% to 3% in a two-level pricing scheme versus a one-level scheme. We conduct operational simulations for validation of solutions and analysis of initial conditions. History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023. Funding: This work was supported by the National Science Foundation [Grant DGE-1650044]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0381 .
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