Optimization Online


Fleet Sizing and Allocation for On-demand Last-Mile Transportation Systems

Karmel S. Shehadeh (kas720***at***lehigh.edu)
Hai Wang (haiwang***at***smu.edu.sg)
Peter Zhang (pyzhang***at***cmu.edu)

Abstract: The last-mile problem refers to the provision of travel service from the nearest public transportation node to a 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. The quality of fleet-allocation 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 a 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.

Keywords: Last mile transportation, demand uncertainty, fleet allocation, stochastic programming, distributionally robust optimization, mixed-integer programming

Category 1: Applications -- OR and Management Sciences

Citation: Shehadeh, K.S., Wang, H., and Zhang, P. (2020). Fleet Sizing and Allocation for On-demand Last-Mile Transportation Systems. Preprint version available at Optimization Online.

Download: [PDF]

Entry Submitted: 10/29/2020
Entry Accepted: 10/29/2020
Entry Last Modified: 01/08/2021

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