Person:
Tovey, Craig A.

Associated Organization(s)
ORCID
ArchiveSpace Name Record

Publication Search Results

Now showing 1 - 4 of 4
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    Fundamentals and applications of connect the dots methods
    (Georgia Institute of Technology, 2010-08-01) Huo, Xiaoming ; Tovey, Craig A.
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    Improving Operations at Manheim Seattle
    (Georgia Institute of Technology, 2008-12-10) Fetic, Sanin ; Gordillo, Alejandro ; Gouws, Jacques ; Guzman, Jorge ; Mills, Lindsay ; Reinhard, Margaret ; Tovey, Craig A.
    The team recommended facility and operational changes for Manheim, the world's largest automobile resale company, at its Seattle site. The changes should increase net revenue by 14%, or $5.4 million annually, at a capital cost of $850,000. The team also clarified the impact of short queues during auctions, and performed a pilot study productivity comparison of 77 auction sites. Because of the company-wide implications of these analyses, the group will reprise its report at the parent company headquarters, Cox enterprises, to executives of both companies. Recommendations were derived from innovative data capture, detailed simulations, 3D facility layout models, and optimization-based productivity assessment.
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    Understanding and improving on-line planning methods
    (Georgia Institute of Technology, 2006-06-01) Tovey, Craig A.
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    Greedy Mapping of Terrain
    (Georgia Institute of Technology, 2001) Koenig, Sven ; Halliburton, William ; Tovey, Craig A.
    We study a greedy mapping method that always moves the robot from its current location to the closest location that it has not visited (or observed) yet, until the terrain is mapped. Although one does not expect such a simple mapping method to minimize the travel distance of the robot, we present analytical results that show (perhaps surprisingly) that the travel distance of the robot is reasonably small. This is interesting because greedy mapping has a number of desirable properties. It is simple to implement and integrate into complete robot architectures. It does not need to have control of the robot at all times, takes advantage of prior knowledge about parts of the terrain (if available), and can be used by several robots cooperatively.