Fleet sizing and allocation for on-demand last-mile transportation systems

• Main Authors: SHEHADEH, Karmel, WANG, Hai, ZHANG, Peter
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Last-mile transportation; on-demand transportation; fleet sizing and allocation; demand uncertainty; stochastic optimization; Artificial Intelligence and Robotics; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/6694; https://ink.library.smu.edu.sg/context/sis_research/article/7697/viewcontent/2107.12523.pdf
• Institution: Singapore Management University

The last-mile problem refers to the provision of travel service from the nearest public transportation node to home or other destination. Last-Mile Transportation Systems (LMTS), which have recently emerged, provide on-demand shared transportation. In this paper, we investigate the fleet sizing and allocation problem for the on-demand LMTS. Specifically, we consider the perspective of a last-mile service provider who wants to determine the number of servicing vehicles to allocate to multiple last-mile service regions in a particular city. In each service region, passengers demanding last-mile services arrive in batches, and allocated vehicles deliver passengers to their final destinations. The passenger demand (i.e., the size of each batch of passengers) is random and hard to predict in advance, especially with limited data during the planning process. The quality of fleetallocation decisions is a function of vehicle fixed cost plus a weighted sum of passenger’s waiting time before boarding a vehicle and in-vehicle riding time. We propose and analyze two models—a stochastic programming model and a distributionally robust optimization model—to solve the problem, assuming known and unknown distribution of the demand, respectively. We conduct extensive numerical experiments to evaluate the models and discuss insights and implications into the optimal fleet sizing and allocation for the on-demand LMTS under demand uncertainty.