Abstract: Aiming at the problem of low energy consumption scheduling in flexible job shop, the energy consumption characteristics and makespan of machine tool operation mode were studied, and the multi-objective optimization model with energy consumption and makespan as the objective function was established. Combined with the characteristics of the model, the target weighting method was used to find the minimum of two variables of energy consumption and makespan. For the single chromosome of genetic algorithm could not accurately express the problem solution when solving more complicated problems, the multi-layer coding strategy was designed to optimize the workpiece processing sequence and machine tool selection during the manufacturing process, and realize low energy consumption flexible job shop scheduling. The production examples were simulated in Matlab. The experimental results show that the proposed scheduling strategy based on improved genetic algorithm is feasible and effective in the shop scheduling with low energy requirements. Decision makers can choose among a range of feasible solutions according to their preferences, and improve the rationality and scientificity of the solution.
Key words: flexible job shop（FJSP）; multi-objective scheduling; multi-layer coding; genetic algorithm