An Integrated Optimization Algorithm of Cutting Parameters and Scheduling for Flexible Job Shop

Environmental problems are becoming more and more serious. Manufacturing industry has the characteristics of high energy consumption and high pollution. Therefore, how to decrease the carbon emissions of the processing process is of great research significance. A hybrid particle swarm optimization algorithm is proposed for the integrated optimization of cutting parameters and flexible job shop scheduling problem that minimizes makespan and carbon emissions. The hybrid initialization method is proposed to ensure the diversity and quality of the initial population. An update method based on mutation operation, crossover operation and Jaya algorithm is designed to make particles jump out of local optimum and improve the search speed. The pareto sorting and crowding degree are used to evaluate the individuals in the population. The external memory pool is introduced to improve the population diversity and avoid premature. Finally, the effectiveness of the hybrid particle swarm optimization algorithm is verified by experiments.