Abstract:  Aiming at the lean problem of heavy equipment processing and assembly integration scheduling, an improved quantum genetic algorithm (SQGA) was proposed to study the multi-product complete job-shop scheduling. The multi-product complete job-shop scheduling optimization model was established with the optimization objectives of processing cost, precise delivery, and transshipment times across the workshop. Combining with the characteristics of product processing and assembly, the coding method based on assembly constraints was designed. In order to avoid the quantum genetic algorithm falling into premature, it was combined with the simulated annealing algorithm with strong local searching ability, the improved quantum genetic algorithm (SQGA) was designed to improve the global searching precision. At the same time, the adaptive rotation angle was designed to make the convergence rate of the population more stable. MATLAB was used to simulate the complete job-shop scheduling calculation example and production example. The results show that the improved quantum genetic algorithm has better convergence effect and solution precision, with the average convergence algebra reducing by 18.6% and the average optimal solution proportion increasing by 26%. In the production example, the processing cost is reduced by 7.8%, the number of transshipments across workshops is reduced by 30.4%, the products achieve accurate delivery, and the lean index of heavy equipment production scheduling is improved.

Key words:  heavy equipment; processing and assembly; complete jobshop scheduling problem(CJSSP); quantum genetic algorithm (QGA)