Learning Framework for Robust Planning and Real-Time Execution Control
Gi-Nam Wang, Gang Yu · Management Science and Financial Engineering · 2002
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 mapping could be applied for solving disruption recovery problems. Two decomposed-reverse neural mappings . N-K-M and M-K-N are employed to produce transportation solutions in real-time. A potential extension is also discussed using the proposed mapping principle and other hybrid heuristics Experimental results are provided to verity the proposed approach.