Multiagent-Based Dynamic Deployment Planning in RTLS-Enabled Automotive Shipment Yard

Authors:
Jindae Kim;Changsoo Ok;Soundar R. Kumara;Shang-Tae Yee
Affiliations:
Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, Univeristy Park, PA 16802, USA;Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, Univeristy Park, PA 16802, USA;Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, Univeristy Park, PA 16802, USA;Manufacturing Systems Research Laboratory, General Motors, Warren, MI 48090, USA
Venue:
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Year:
2007

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Abstract

Real-time vehicle location information enables to facilitate more efficient decision-making in dynamic automotive shipment yard environment. This paper proposes a multiagent-based decentralized decision-making model for the vehicle deployment planning in a shipment yard. A multiagent architecture is designed to facilitate decentralized algorithms and coordinate different agents dynamically. The results of computational experiments show that the proposed deployment model outperforms a current deployment practice with respect to the deployment performance measures.