Dynamic Multi-Vehicle Routing with Multiple Classes of Demands
Name
Pavone-2009-Dynamic Multi-Vehicle Routing with Multiple Classes of Demands.pdf
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492.37 KB
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Author(s)
Pavone, Marco
Smith, Stephen L.
Bullo, Francesco
Frazzoli, Emilio
Date Issued
July 2009
Journal
American Control Conference, 2009. ACC '09
Publisher
Institute of Electrical and Electronics Engineers
Citation
Pavone, M. et al. “Dynamic multi-vehicle routing with multiple classes of demands.” American Control Conference, 2009. ACC '09. 2009. 604-609. ©2009 IEEE.
Version
Final published version
Abstract
In this paper we study a dynamic vehicle routing problem in which there are multiple vehicles and multiple classes of demands. Demands of each 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 (and thus the optimal), 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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DOI of Published Version
https://doi.org/10.1109/ACC.2009.5160557
