The XML handbook (2nd ed.)
Using simulation to evaluate site traffic at an automobile truck plant
Proceedings of the 33nd conference on Winter simulation
Proceedings of the 35th conference on Winter simulation: driving innovation
Manufacturing case studies: generic case studies for manufacturing simulation applications
Proceedings of the 35th conference on Winter simulation: driving innovation
Stress testing a supply chain using simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Distributed simulation for interoperability testing along the supply chain
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Representing layout information in the CMSD specification
Proceedings of the 40th Conference on Winter Simulation
Enabling flexible manufacturing systems by using level of automation as design parameter
Winter Simulation Conference
Effect of layout concept on the performance measures in automotive body shop
Proceedings of the 2012 SpringSim Poster & Work-In-Progress Track
Proceedings of the Winter Simulation Conference
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Computer simulation is effective in improving the efficiency of manufacturing system design, operation, and maintenance. Most simulation models are usually tailored to address a narrow set of industrial issues, e. g., the introduction of a new product. If generic data-driven simulations could be developed they would be reusable for wider application including interoperability testing of standards for exchange of data across the supply chain in manufacturing. To facilitate future interoperability testing and training, scientists at the National Institute of Standards and Technology are currently developing distributed, integrated manufacturing simulations for automotive manufacturing. These simulations are being developed at four different levels: the supply chain, the assembly plant, the engineering systems, and the shop floor level. This paper describes the development of a simulation model of the final assembly plant. Future efforts will increase the versatility of the model, run it on neutral data and extend integration with supply chain simulation.