Bandit Algorithms for Online Learning and Resource Allocation
Online platforms are emerging as a powerful mechanism for matching resources to requests. In the setting of freight, the requests arrive from shippers, who have a diverse collection of goods. The resources are supplied by shippers (trucks), and have various physical constraints (driver’s route preferences, carrying capacity, geographic preferences, etc.). Online platforms are emerging that (a) learn the characteristics of shippers and carriers, and (b) efficiently match goods to trucks based on such learning. This project will develop algorithms for such online resource allocation. This is a challenging problem, due to the complexity of the learning tasks. Such algorithms can have considerable impact on efficiently using trucking resources.
- Record URL:
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Supplemental Notes:
- Final report: http://ctr.utexas.edu/wp-content/uploads/146.pdf
Language
- English
Project
- Status: Completed
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Contract Numbers:
DTRT13-G-UTC58
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Project Managers:
Bhat, Chandra
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Performing Organizations:
Data-Supported Transportation Operations and Planning Center
University of Texas at Austin
Austin, TX United States 78701 -
Principal Investigators:
Shakkottai, Sanjay
- Start Date: 20170101
- Expected Completion Date: 20180831
- Actual Completion Date: 0
- Source Data: 146
Subject/Index Terms
- TRT Terms: Algorithms; Bearing capacity; Freight brokers; Learning; Route choice; Shipper demand; Stated preferences; Trucks; Urban goods movement
- Subject Areas: Data and Information Technology; Freight Transportation; Highways;
Filing Info
- Accession Number: 01634957
- Record Type: Research project
- Source Agency: Data-Supported Transportation Operations and Planning Center
- Contract Numbers: DTRT13-G-UTC58
- Files: UTC, RIP
- Created Date: May 18 2017 9:16PM