To solve the problem of impediments of production plans and decline of production efficiency caused by potential time-varying effects of the machine condition uncertainty in the semiconductor production process

decision-making methods for wafer production sequence scheduling considering time-changing effects are developed. Firstly

with collecting historical processing time data

the relevance between characteristics of machine condition variation and time-changing effects of wafer processing times is diagnosed to establish parallel machine scheduling model considering time-changing effects. The target is to minimize the makespan. A hybrid search algorithm with optimal scheduling knowledge (HSAOSK) is designed based on optimal single machine scheduling rules and multi-machine scheduling optimization knowledge to reduce the searching space and improve the calculation efficiency. Computational experiments show that the optimal solution of the HSAOSK algorithm is the same as the exact algorithm to solve small-scale cases. As for the large scale cases

comparing to the other algorithms

the optimal makespan of HSAOSK algorithm has 6.17% decrement with the shortest time consumption. The HSAOSK algorithm can meet the needs of constructing a semiconductor scheduling decision-making scheme.