As the core of steel production， hot rolling process has demands of strict production continuity and complex production technology. The random arrival of rush orders and urgent delivery requirements have adverse impacts on production continuity and quality stability. Aiming at those kind of dynamic events of rush order insertion， a hot rolling rescheduling optimization method was proposed. Firstly， the influence of order disturbance factor on the scheduling scheme was analyzed， and a mathematical model of hot rolling rescheduling was established with the optimization objective of minimizing the weighted sum of tardiness of orders and jump penalty of slabs. Then， an Estimation of Distribution Algorithm （EDA） for hot rolling rescheduling was designed. In this algorithm， aiming at the insertion processing of rush orders， an integer encoding scheme was proposed based on the insertion position， the probability model based on the characteristics of the model was designed， and the fitness function based on the penalty value was defined by considering the targets and constraints comprehensively. The feasibility and validity of the model and the algorithm were verified by the simulation experiment on the actual production data.

The deformation of 3D models is one of the hot issues of computer graphics and other cross-discipline research. In view of the artificially using the linear strain on the model deformation, the authors proposed an effective model deformation approach. First, the original model was simplified through geometric delete method and the simplified models stayed as the form of the original model. Then, using the tetrahedron subdivision, the tetrahedral model was deformed by adopting the Green strain. Finally, the experimental results between linear strain and non-linear strain were presented in detail. The result also demonstrates that first simplifying the models then generating the tetrahedron, the simulation rate could be increased.