Integrated carbon footprint with cutting parameters for production scheduling

Abstract

A large part of the global carbon footprint comes from manufacturing. Apart from material distribution during product assembly, rational optimization of cutting parameters and production scheduling during processing are the main ways to realize low-carbon manufacturing. The existing researches mainly focus on cutting parameters or production scheduling when reducing manufacturing carbon emissions, but seldom considers the coupling relationship between the two. This limits the reduction of emissions to some extent. On this basis, a collaborative low-carbon optimization system suitable for the actual use of enterprises was developed. The optimization of production scheduling is a discrete problem, while cutting parameters are continuous and processing time brought by different cutting parameters will cause different effects on the scheduling scheme. Therefore, a two-stage optimization strategy is adopted to achieve collaborative optimization of cutting parameters and production scheduling. To achieve multi-objective optimization of the model and speed up convergence, the Crowding Niche mechanism and elite retention strategy are introduced into the selection operator to improve the genetic algorithm based on Pareto Optimality. In addition, different from the traditional that need to manually determine the machine tool and cutting tool according to the demand, a knowledge base module is integrated into the system. The knowledge base module is qualified for the knowledge acquisition and instance matching of the model in order to meet the processing capacity and constraints and enhance the ability of intelligent decision-making during production. Finally, the interactive application interface of the collaborative low-carbon optimization system has been implemented and the effectiveness of the system is verified by taking the manufacturing case of electromechanical enterprises as an example.