Abstract: The collaborative optimization of manufacturing process and energy consumption is one of the hot research issues in intelligent optimization manufacturing. To solve the scheduling optimization problem of discrete manufacturing process, a coordinated scheduling optimization of production and energy consumption with the shortest processing time and lowest energy consumption is established. The model proposes an improved differential evolution algorithm based on adaptive mutation and crossover probability factor to solve the optimal scheduling problem. By establishing the operation-based coding method, the application of continuous algorithm in discrete optimization scheduling problem is realized by using ascending ordering rules. The effectiveness of the proposed algorithm is verified by simulation, and the performances of particle swarm optimization algorithm, genetic algorithm and the improved algorithm are compared. The results show that the target solution obtained by this algorithm is significantly better than the other two algorithms, which verifies the superiority of the algorithm.

Key words: collaborative optimization scheduling, differential evolution algorithm, self-adaptive operator, discrete manufacturing process