A carbon efficiency upgrading method for mechanical machining based on scheduling optimization strategy

Abstract

Low-carbon manufacturing (LCM) is increasingly being regarded as a new sustainable manufacturing model of carbon emission reduction in the manufacturing industry. In this paper, a two-stage low-carbon scheduling optimization method of job shop is presented as part of the efforts to implement LCM, which also aims to reduce the processing cost and improve the efficiency of a mechanical machining process. In the first stage, a task assignment optimization model is proposed to optimize carbon emissions without jeopardizing the processing efficiency and the profit of a machining process. Non-dominated sorting genetic algorithm II and technique for order preference by similarity to an ideal solution are then adopted to assign the most suitable batch task of different parts to each machine. In the second stage, a processing route optimization model is established to plan the processing sequence of different parts for each machine. Finally, niche genetic algorithm is utilized to minimize the makespan. A case study on the fabrication of four typical parts of a machine tool is demonstrated to validate the proposed method.

Keywords

Low-carbon manufacturing / carbon efficiency / multi-objective optimization / two-stage scheduling / job shop

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Acknowledgements

The work described in this paper was supported by China Postdoctoral Science Foundation (Grant No. 2018M642935), the Plateau Disciplines in Shanghai, and the National Natural Science Foundation of China (Grant Nos. 51905392, 51775392 and 51675388). These financial contributions are gratefully acknowledged.

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