Tactical Design of Same-Day Delivery Systems

We study tactical models for the design of same-day delivery (SDD) systems. Same-day fulfillment in e-commerce has seen substantial growth in recent years, and the underlying management of such services is complex. Although the literature includes operational models to study SDD, they tend to be detailed, complex, and computationally difficult to solve, and thus may not provide any insight into tactical SDD design variables and their impact on the average performance of the system. We propose a simplified vehicle-dispatching model that captures the “average” behavior of an SDD system from a single stocking location by utilizing continuous approximation techniques. We analyze the structure of optimal vehicle-dispatching policies given our model for two important instance families—the single-vehicle case and the case in which the delivery fleet is large—and develop techniques to find these policies that require only simple computations. We also leverage these results to analyze the case of a finite fleet, proposing a heuristic policy with a worst-case approximation guarantee. We then demonstrate with several example problem settings how this model and these policies can help answer various tactical design questions, including how to select a fleet size, determine an order cutoff time, and combine SDD and overnight order delivery operations. We validate model predictions empirically against a detailed operational model in a computational case study using geographic and Census data for the northeastern metro Atlanta region, and we demonstrate that our model predicts the average number of orders served and dispatch time to within 1%. This paper was accepted by Jay Swaminathan, operations management.

A queueing-theoretic framework for vehicle dispatching in dynamic car-hailing

With the rapid development of smart mobile devices, the car-hailing platforms (e.g., Uber or Lyft) have attracted much attention from the academia and the industry. In this paper, we consider a dynamic car-hailing problem, namely maximum revenue vehicle dispatching (MRVD), in which rider requests dynamically arrive and drivers need to serve riders such that the entire revenue of the platform is maximized. We prove that the MRVD problem is NP-hard and intractable. To handle the MRVD problem, we propose a queueing-based vehicle dispatching framework, which first uses existing machine learning models to predict the future vehicle demand of each region, then estimates the idle time periods of drivers through a double-sided queueing model for each region. With the information of the predicted vehicle demands and estimated idle time periods of drivers, we propose two batch-based vehicle dispatching algorithms to efficiently assign suitable drivers to riders such that the expected overall revenue of the platform is maximized during each batch processing. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets. In summary, our methods can achieve 3% ~ 10% increase on overall revenue without sacrificing on running speed compared with the state-of-the-art solutions.