IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i21p8237-d963233.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/21/8237/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/21/8237/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zied Hajej & Nidhal Rezg & Ali Gharbi, 2018. "Quality issue in forecasting problem of production and maintenance policy for production unit," International Journal of Production Research, Taylor & Francis Journals, vol. 56(18), pages 6147-6163, September.
    2. Hassan, Taimoor & Song, Huaming & Khan, Yasir & Kirikkaleli, Dervis, 2022. "Energy efficiency a source of low carbon energy sources? Evidence from 16 high-income OECD economies," Energy, Elsevier, vol. 243(C).
    3. Bouslah, B. & Gharbi, A. & Pellerin, R., 2016. "Joint economic design of production, continuous sampling inspection and preventive maintenance of a deteriorating production system," International Journal of Production Economics, Elsevier, vol. 173(C), pages 184-198.
    4. Xia, Tangbin & Si, Guojin & Shi, Guo & Zhang, Kaigan & Xi, Lifeng, 2022. "Optimal selective maintenance scheduling for series–parallel systems based on energy efficiency optimization," Applied Energy, Elsevier, vol. 314(C).
    5. Qinming Liu & Zhinan Li & Tangbin Xia & Minchih Hsieh & Jiaxiang Li, 2022. "Integrated Structural Dependence and Stochastic Dependence for Opportunistic Maintenance of Wind Turbines by Considering Carbon Emissions," Energies, MDPI, vol. 15(2), pages 1-21, January.
    6. Bermeo-Ayerbe, Miguel Angel & Ocampo-Martinez, Carlos & Diaz-Rozo, Javier, 2022. "Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems," Energy, Elsevier, vol. 238(PB).
    7. Jingyi Zhao & Chunhai Gao & Tao Tang, 2022. "A Review of Sustainable Maintenance Strategies for Single Component and Multicomponent Equipment," Sustainability, MDPI, vol. 14(5), pages 1-22, March.
    8. Aiping Jiang & Ning Dong & Kwok Leung Tam & Chonghao Lyu, 2018. "Development and Optimization of a Condition-Based Maintenance Policy with Sustainability Requirements for Production System," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-19, February.
    9. Brundage, Michael P. & Chang, Qing & Zou, Jing & Li, Yang & Arinez, Jorge & Xiao, Guoxian, 2015. "Energy economics in the manufacturing industry: A return on investment strategy," Energy, Elsevier, vol. 93(P2), pages 1426-1435.
    10. Shang, Zhendong & Gao, Dong & Jiang, Zhipeng & Lu, Yong, 2019. "Towards less energy intensive heavy-duty machine tools: Power consumption characteristics and energy-saving strategies," Energy, Elsevier, vol. 178(C), pages 263-276.
    11. L. Tlili & M. Radhoui & A. Chelbi, 2015. "Condition-Based Maintenance Strategy for Production Systems Generating Environmental Damage," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-12, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sinisterra, Wilfrido Quiñones & Lima, Victor Hugo Resende & Cavalcante, Cristiano Alexandre Virginio & Aribisala, Adetoye Ayokunle, 2023. "A delay-time model to integrate the sequence of resumable jobs, inspection policy, and quality for a single-component system," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Chen, Zhaoxiang & Chen, Zhen & Zhou, Di & Pan, Ershun, 2023. "Energy-oriented opportunistic maintenance optimization of continuous process manufacturing systems with two types of stochastic durations," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Wang, Jinling & Tian, Yebing & Hu, Xintao & Han, Jinguo & Liu, Bing, 2023. "Integrated assessment and optimization of dual environment and production drivers in grinding," Energy, Elsevier, vol. 272(C).
    4. Hlioui, Rached & Gharbi, Ali & Hajji, Adnène, 2017. "Joint supplier selection, production and replenishment of an unreliable manufacturing-oriented supply chain," International Journal of Production Economics, Elsevier, vol. 187(C), pages 53-67.
    5. Giacomo Di Foggia & Massimo Beccarello & Marco Borgarello & Francesca Bazzocchi & Stefano Moscarelli, 2022. "Market-Based Instruments to Promote Energy Efficiency: Insights from the Italian Case," Energies, MDPI, vol. 15(20), pages 1-16, October.
    6. Sun, Jingchao & Na, Hongming & Yan, Tianyi & Che, Zichang & Qiu, Ziyang & Yuan, Yuxing & Li, Yingnan & Du, Tao & Song, Yanli & Fang, Xin, 2022. "Cost-benefit assessment of manufacturing system using comprehensive value flow analysis," Applied Energy, Elsevier, vol. 310(C).
    7. Zheng, Li & Yuan, Ling & Khan, Zeeshan & Badeeb, Ramez Abubakr & Zhang, Leilei, 2023. "How G-7 countries are paving the way for net-zero emissions through energy efficient ecosystem?," Energy Economics, Elsevier, vol. 117(C).
    8. Jessica Walther & Matthias Weigold, 2021. "A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry," Energies, MDPI, vol. 14(4), pages 1-24, February.
    9. Khan, Yasir & Hassan, Taimoor & Guiqin, Huang & Nabi, Ghulam, 2023. "Analyzing the impact of natural resources and rule of law on sustainable environment: A proposed policy framework for BRICS economies," Resources Policy, Elsevier, vol. 86(PA).
    10. Xie, Peijun & Jamaani, Fouad, 2022. "Does green innovation, energy productivity and environmental taxes limit carbon emissions in developed economies: Implications for sustainable development," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 66-78.
    11. Guan, Zepeng & Hossain, Mohammad Razib & Sheikh, Muhammad Ramzan & Khan, Zeeshan & Gu, Xiao, 2023. "Unveiling the interconnectedness between energy-related GHGs and pro-environmental energy technology: Lessons from G-7 economies with MMQR approach," Energy, Elsevier, vol. 281(C).
    12. Palander, Teijo & Haavikko, Hanna & Kärhä, Kalle, 2018. "Towards sustainable wood procurement in forest industry – The energy efficiency of larger and heavier vehicles in Finland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 100-118.
    13. Gössinger, Ralf & Helmke, Hanna & Kaluzny, Michael, 2017. "Condition-based release of maintenance jobs in a decentralised production-maintenance system – An analysis of alternative stochastic approaches," International Journal of Production Economics, Elsevier, vol. 193(C), pages 528-537.
    14. Xiao, Qinge & Li, Congbo & Tang, Ying & Pan, Jian & Yu, Jun & Chen, Xingzheng, 2019. "Multi-component energy modeling and optimization for sustainable dry gear hobbing," Energy, Elsevier, vol. 187(C).
    15. Azimpoor, Samareh & Taghipour, Sharareh, 2021. "Joint inspection and product quality optimization for a system with delayed failure," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    16. Zou, Jing & Chang, Qing & Arinez, Jorge & Xiao, Guoxian, 2017. "Data-driven modeling and real-time distributed control for energy efficient manufacturing systems," Energy, Elsevier, vol. 127(C), pages 247-257.
    17. Cheng, Guoqing & Li, Ling, 2020. "Joint optimization of production, quality control and maintenance for serial-parallel multistage production systems," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    18. Zhu, Mixin & Zhou, Xiaojun, 2023. "Hierarchical-clustering-based joint optimization of spare part provision and maintenance scheduling for serial-parallel multi-station manufacturing systems," International Journal of Production Economics, Elsevier, vol. 264(C).
    19. Ágota Bányai, 2021. "Energy Consumption-Based Maintenance Policy Optimization," Energies, MDPI, vol. 14(18), pages 1-33, September.
    20. Cai, Wei & Wang, Lianguo & Li, Li & Xie, Jun & Jia, Shun & Zhang, Xugang & Jiang, Zhigang & Lai, Kee-hung, 2022. "A review on methods of energy performance improvement towards sustainable manufacturing from perspectives of energy monitoring, evaluation, optimization and benchmarking," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8237-:d:963233. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.