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Congestion-aware dynamic routing in automated material handling systems

In semiconductor manufacturing, automated material handling systems (AMHSs) transport wafers through a complex re-entrant manufacturing process. In some systems, Overhead Hoist Transport (OHT) vehicles move throughout the facility on a ceiling-mounted track system, delivering wafers to machines and storage locations. To improve efficiency in such systems, this thesis proposes an adaptive dynamic routing approach that allows the system to self-regulate, reducing steady-state travel times by 4-6% and avoiding excessive congestion and deadlock. Our approach allows vehicles to be rerouted while in progress in response to changes in the location and severity of congestion as measured by edge traversal time estimates updated via exponential smoothing. Our proposed method is efficient enough to be used in a large system where several routing decisions are made each second. We also consider how the effectiveness of a AMHS layout differs between static and dynamic routing. We demonstrate that dynamic routing significantly reduces sensitivity to shortcut placement and allows an eight-fold increase in the number of shortcuts along the center loop. This reduces travel times by an additional 24%. To demonstrate the effectiveness of our proposed routing approach, we use a high-fidelity simulation of vehicle movement. To test the impact of routing methods on layout effectiveness, we developed an associated Excel-based automated layout generation tool that allows the efficient generation of thousands of candidate layouts. The user selects from among a set of modular templates to create a design and all simulation files are generated with the click of a button.

Identiferoai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/53013
Date12 January 2015
CreatorsBartlett, Kelly K.
ContributorsNemhauser, George L., Ahmed, Shabbir, Sokol, Joel
PublisherGeorgia Institute of Technology
Source SetsGeorgia Tech Electronic Thesis and Dissertation Archive
Languageen_US
Detected LanguageEnglish
TypeDissertation
Formatapplication/pdf

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