Green scheduling optimization for flexible job shops considering multiple states of machines

With the development of green production and industrial upgrading, the traditional production method of heavy manufacturing industry is in urgent need to change. Against the background that the energy structure cannot be changed in a short time, reasonable scheduling optimization is an effective solution to improve the production efficiency and energy utilization efficiency of enterprises. In the actual processing environment of the surveyed enterprises, the machines can have many different states during operation. These different states greatly increase the flexibility and complexity of the manufacturing shop, and the previous optimization methods are not suitable for this kind of manufacturing environment. For this reason, a multi-objective optimization model of flexible job shop scheduling considering multiple states of machines is proposed. Then, a two-stage optimization method is proposed for optimization. In the first stage, an improved genetic algorithm is proposed to solve the model. In the second stage, the green scheduling heuristic strategy is adopted to optimize the machine states. Finally, the feasibility of the model and the effectiveness of the solution method of this paper are verified by the optimization of practical cases.