|Table of Contents|

[1] Wang Yansen, Feng Lijie, Wang Jinfeng, et al. Multiobjective scheduling optimization of bearing productionworkshop for green manufacturing [J]. Journal of Southeast University (English Edition), 2022, 38 (4): 350-362. [doi:10.3969/j.issn.1003-7985.2022.04.004]
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Multiobjective scheduling optimization of bearing productionworkshop for green manufacturing()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
38
Issue:
2022 4
Page:
350-362
Research Field:
Mechanical Engineering
Publishing date:
2022-12-20

Info

Title:
Multiobjective scheduling optimization of bearing productionworkshop for green manufacturing
Author(s):
Wang Yansen1;  Feng Lijie2;  4;  Wang Jinfeng2;  4;  Liu Peng3;  4;  Zhao Huadong1
1School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China
2China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China
3School of Management, Zhengzhou University, Zhengzhou 450001, China
4Henan Engineering Research Center of Innovation Method, Zhengzhou 450001, China
Keywords:
green shop scheduling problem(GSSP);  multiobjective optimization;  carbon emissions;  rolling bearing
PACS:
TH278;TP30
DOI:
10.3969/j.issn.1003-7985.2022.04.004
Abstract:
Aiming at the machining process of high-performance bearing parts, the green shop scheduling problem of bearing parts processing was studied herein, with the maximum completion time, minimum machine carbon emission, and minimum grinding fluid usage as the optimization objectives. The manufacturing process is divided into six technological processes: startup, clamping, machining, unloading, standby, and shutdown. The multiobjective green shop scheduling mathematical model is established. Then, an improved multiobjective genetic algorithm is proposed, adopting a segmented coding method that integrates the process and machine selections and improves the steps of crossover and mutation, all of which improve the algorithm’s convergence. Finally, the bearing parts processing of a bearing company is taken as a case study, and large-scale data tests and analyses are constructed. The result shows that the proposed model can obtain lower completion time, carbon emission, and grinding fluid consumption, which verifies the scientificity and effectiveness of the proposed model.

References:

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Memo

Memo:
Biographies: Wang Yansen(1996—), male, Ph.D. candidate; Feng Lijie(1966—), male, doctor, professor, ljfeng@shmtu.edu.cn.
Foundation items: Innovation Method Fund of China(No.2019IM020200), Joint Funds of the National Natural Science Foundation of China(No. U1904210-4), Zhengzhou University Support Program Project for Young Talents and Enterprise Cooperative Innovation Team, “Intelligent Manufacturing Comprehensive Standardization and New Model Application Project” of Ministry of Industry and Information Technology(No. 2017ZNZX02), Shanghai Science and Technology Program(No. 20040501300).
Citation: Wang Yansen, Feng Lijie, Wang Jinfeng, et al. Multiobjective scheduling optimization of bearing production workshop for green manufacturing[J].Journal of Southeast University(English Edition), 2022, 38(4):350-362.DOI:10.3969/j.issn.1003-7985.2022.04.004.
Last Update: 2022-12-20