Preventive Maintenance Strategy Optimization in Manufacturing System Considering Energy Efficiency and Quality Cost

Liang Yang, Qinming Liu (), Tangbin Xia, Chunming Ye and Jiaxiang Li
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Liang Yang: Department of Industrial Engineering, Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China
Qinming Liu: Department of Industrial Engineering, Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China
Tangbin Xia: State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai JiaoTong University, SJTU-Fraunhofer Center, Shanghai 200240, China
Chunming Ye: Department of Industrial Engineering, Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China
Jiaxiang Li: Department of Industrial Engineering, Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China

Energies, 2022, vol. 15, issue 21, 1-18

Abstract: Climate change is a serious challenge facing the world today. Countries are already working together to control carbon emissions and mitigate global warming. Improving energy efficiency is currently one of the main carbon reduction measures proposed by the international community. Within this context, improving energy efficiency in manufacturing systems is crucial to achieving green and low-carbon transformation. The aim of this work is to develop a new preventive maintenance strategy model. The novelty of the model is that it takes into account energy efficiency, maintenance cost, product quality, and the impact of recycling defective products on energy efficiency. Based on the relationship between preventive maintenance cost, operating energy consumption, and failure rate, the correlation coefficient is introduced to obtain the variable preventive maintenance cost and variable operating energy consumption. Then, the cost and energy efficiency models are established, respectively, and finally, the Pareto optimal solution is found by the nondominated sorting genetic algorithm II (NSGAII). The results show that the preventive maintenance strategy proposed in this paper is better than the general maintenance strategy and more relevant to the actual situation of manufacturing systems. The scope of the research in this paper can support the decision of making energy savings and emission reductions in the manufacturing industry, which makes the production, maintenance, quality, and architecture of the manufacturing industry optimized.

Keywords: preventive maintenance; energy efficiency; quality cost; multiobjective optimization; manufacturing system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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