Abstract: In view of the problem of disconnection between actual production scheduling and theoretical production scheduling due to frequent replacement of tools,fixtures,etc. in high-complexity intelligent manufacturing scenarios with multiple varieties and variable batches, it defines two coefficients,Machine Preparation Duration (MPD) and Machine Processing Coefficient (MPC), constructs an intelligent workshop scheduling mathematical model with multiple varieties and variable batches that introduces MPC parameters and takes minimizing maximum processing machine duration, the total machine load time and the total machine preparation time as the objective functions, designs a new solving algorithm Non-dominated Sorting Immune Genetic Algorithm-Ⅱ (NSIGA-Ⅱ) that combines Non-dominated Sorting Genetic Algorithm-Ⅱ (NGSA-Ⅱ) and Immune Genetic Algorithm (IGA). This algorithm uses a variety of methods for initialization,and proposes a scoring strategy that comprehensively considers non-dominated sorting and the size of the objective function value to select good individuals. At the same time,in order to improve the diversity of the population,it introduces the strategy of population stratification and adaptive cross-mutation. Finally,multiple sets of comparative experiments verify the effectiveness of this algorithm and its advantages of good stability and high solution quality when exploring optimal solutions.

Key words: machine preparation duration, NGSA-Ⅱ, immune genetic algorithm, intelligent shop scheduling