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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Liang Yang & Qinming Liu & Tangbin Xia & Chunming Ye & Jiaxiang Li, 2022. "Preventive Maintenance Strategy Optimization in Manufacturing System Considering Energy Efficiency and Quality Cost," Energies, MDPI, vol. 15(21), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8237-:d:963233
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    References listed on IDEAS

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