Management Science and Financial Engineering

The Korean Operations Research and Management Science Society (한국경영과학회)
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Learning Framework for Robust Planning and Real-Time Execution Control

  • Wang, Gi-Nam (Dept. of Industrial and Information Systems Engineering, Ajou University) ;
  • Yu, Gang (Dept. of Management Science and Information Systems, McCombs School of Business, The University of Texas at Austin, Austin, TX78712, USA)
  • Published : 2002.05.01
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Abstract

In this Paper, an attempt is made to establish a learning framework for robust planning and real-time execution control. Necessary definitions and concepts are clearly presented to describe real-time operational control in response to Plan disruptions. A general mathematical framework for disruption recovery is also laid out. Global disruption model is decomposed into suitable number of local disruption models. Execution Pattern is designed to capture local disruptions using decomposed-reverse neural mappings, and to further demonstrate how the decomposed-reverse mappings could be applied for solving disrubtion recovery problems. Two decomposed-reverse neural mappings, N-K-M and M-K-N are employed to produce transportation solutions in react-time. A potential extension is also discussed using the proposed mapping principle and other hybrid heuristics. Experimental results are provided to verify the proposed approach.

Keywords

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