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Mathematical Problems in Engineering
Volume 2015, Article ID 575402, 15 pages
Research Article

Modeling of RFID-Enabled Real-Time Manufacturing Execution System in Mixed-Model Assembly Lines

1Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macau
2School of Management, Henan University of Science and Technology, Luoyang 471000, China

Received 7 May 2014; Revised 9 July 2014; Accepted 14 July 2014

Academic Editor: Qingsong Xu

Copyright © 2015 Zhixin Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To quickly respond to the diverse product demands, mixed-model assembly lines are well adopted in discrete manufacturing industries. Besides the complexity in material distribution, mixed-model assembly involves a variety of components, different process plans and fast production changes, which greatly increase the difficulty for agile production management. Aiming at breaking through the bottlenecks in existing production management, a novel RFID-enabled manufacturing execution system (MES), which is featured with real-time and wireless information interaction capability, is proposed to identify various manufacturing objects including WIPs, tools, and operators, etc., and to trace their movements throughout the production processes. However, being subject to the constraints in terms of safety stock, machine assignment, setup, and scheduling requirements, the optimization of RFID-enabled MES model for production planning and scheduling issues is a NP-hard problem. A new heuristical generalized Lagrangian decomposition approach has been proposed for model optimization, which decomposes the model into three subproblems: computation of optimal configuration of RFID senor networks, optimization of production planning subjected to machine setup cost and safety stock constraints, and optimization of scheduling for minimized overtime. RFID signal processing methods that could solve unreliable, redundant, and missing tag events are also described in detail. The model validity is discussed through algorithm analysis and verified through numerical simulation. The proposed design scheme has important reference value for the applications of RFID in multiple manufacturing fields, and also lays a vital research foundation to leverage digital and networked manufacturing system towards intelligence.