Dynamic Vehicle Routing with Priority Classes of Stochastic Demands
Name
Smith-2010-DYNAMIC VEHICLE ROUT.pdf
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508.38 KB
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Checksum (MD5)
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Author(s)
Smith, Stephen L.
Pavone, Marco
Bullo, Francesco
Frazzoli, Emilio
Date Issued
January 2010
Journal
SIAM Journal on Control and Optimization
Publisher
Society of Industrial and Applied Mathematics
Citation
Smith, Stephen L. et al. “Dynamic Vehicle Routing with Priority Classes of Stochastic Demands.” SIAM Journal on Control and Optimization 48.5 (2010): 3224-3245. ©2010 Society for Industrial and Applied Mathematics
Version
Final published version
Abstract
In this paper we introduce a dynamic vehicle routing problem in which there are multiple vehicles and multiple priority classes of service demands. Service demands of each priority class arrive in the environment randomly over time and require a random amount of on-site service that is characteristic of the class. To service a demand, one of the vehicles must travel to the demand location and remain there for the required on-site service time. The quality of service provided to each class is given by the expected delay between the arrival of a demand in the class and that demand's service completion. The goal is to design a routing policy for the service vehicles which minimizes a convex combination of the delays for each class. First, we provide a lower bound on the achievable values of the convex combination of delays. Then, we propose a novel routing policy and analyze its performance under heavy-load conditions (i.e., when the fraction of time the service vehicles spend performing on-site service approaches one). The policy performs within a constant factor of the lower bound, where the constant depends only on the number of classes, and is independent of the number of vehicles, the arrival rates of demands, the on-site service times, and the convex combination coefficients
Subjects
vehicle routing
optimization
multiagent systems
heterogeneous systems
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1137/090749347
