IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/4187575.html
   My bibliography  Save this article

Development and Optimization of a Condition-Based Maintenance Policy with Sustainability Requirements for Production System

Author

Listed:
  • Aiping Jiang
  • Ning Dong
  • Kwok Leung Tam
  • Chonghao Lyu

Abstract

In the field of condition-based maintenance, maintenance costs and system reliability criteria are the primary considerations for traditional maintenance management. These methods lack consideration of the environmental impact caused by equipment degradation, such as excessive emissions and energy consumption. In addition, because equipment degradation has various impacts on the ecological environment, companies with excessive emissions and energy consumption can receive huge fines, making it of great value to study ecoconscious maintenance strategies. In this paper, we propose a condition-based maintenance strategy considering energy consumption and carbon dioxide emissions. The major objective of the research is to extend a model which integrates ecological aspects with maintenance decision-making and optimization. The simulation and sensitivity analyses conducted verify that the model proposed can minimize total costs, as well as the environmental impact.

Suggested Citation

  • 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.
  • Handle: RePEc:hin:jnlmpe:4187575
    DOI: 10.1155/2018/4187575
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2018/4187575.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2018/4187575.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2018/4187575?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Ágota Bányai, 2021. "Energy Consumption-Based Maintenance Policy Optimization," Energies, MDPI, vol. 14(18), pages 1-33, September.

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:4187575. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.