Scheduling optimization for highly customized shop assembly production based on improved NSGA-II algorithm

With the gradual rise of the development trend of informatization and intelligence in industrial manufacturing, an improved NSGA-II algorithm is proposed to study the problems of numerous orders, long completion time, low equipment utilization, and long time to generate scheduling solutions only by MES in the assembly production of highly customized workshops, which constructs an intelligent scheduling model with the minimum completion time and the maximum equipment loading rate. Adopting adaptive crossover and variational probability design allows the algorithm to better adapt to the changes in the solution space during the search process. On the initialization of the next generation population after each iteration, a local search is performed based on the optimized solution set of the previous generation, which improves the algorithm's convergence speed and the optimized solution's accuracy. Adjusting the calculation order of fast nondominated ordering and congestion distance during the iteration process and still calculating fast non-dominated ordering and congestion distance after the iteration is completed, the optimal solution can be screened out and the algorithm's performance can be significantly improved. A simulation experiment of scheduling a complex order of a military product is designed to verify the feasibility and effectiveness of the improved algorithm in intelligent scheduling.