Partheno-genetic algorithm based adaptive dispatching method for semiconductor scheduling

This study aims to solve the scheduling problem of batch processing and reentrant in semiconductor manufacturing by using a partheno-genetic algorithm based adaptive dispatching method (PGABADM). First, partheno-genetic algorithm is proposed to solve the scheduling problem according to their characteristics. Then it generates solutions for the jobs with different distribution of arrival time and due date. These solutions are taken as learning samples. Second, we analyze influencing factors by sample learning method from those solutions. Finally, with the help of linear regression, the coefficients of influencing factors can be calculated to build a dynamic dispatching rule adaptive to running environments. This proposed method is better than traditional ways (FIFO and EDD) according to the simulation results.