IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v4y2011i9p1391-1409d13951.html
   My bibliography  Save this article

Improved Methods for Production Manufacturing Processes in Environmentally Benign Manufacturing

Author

Listed:
  • Xian-Chun Tan

    (Institute of Policy and Management, Chinese Academy of Sciences, Haidian, Beijing 100190, China)

  • Yan-Yan Wang

    (Institute of Policy and Management, Chinese Academy of Sciences, Haidian, Beijing 100190, China)

  • Bai-He Gu

    (Institute of Policy and Management, Chinese Academy of Sciences, Haidian, Beijing 100190, China)

  • Ze-Kun Mu

    (Institute of Policy and Management, Chinese Academy of Sciences, Haidian, Beijing 100190, China)

  • Can Yang

    (Institute of Policy and Management, Chinese Academy of Sciences, Haidian, Beijing 100190, China)

Abstract

How to design a production process with low carbon emissions and low environmental impact as well as high manufacturing performance is a key factor in the success of low-carbon production. It is important to address concerns about climate change for the large carbon emission source manufacturing industries because of their high energy consumption and environmental impact during the manufacturing stage of the production life cycle. In this paper, methodology for determining a production process is developed. This methodology integrates process determination from three different levels: new production processing, selected production processing and batch production processing. This approach is taken within a manufacturing enterprise based on prior research. The methodology is aimed at providing decision support for implementing Environmentally Benign Manufacturing (EBM) and low-carbon production to improve the environmental performance of the manufacturing industry. At the first level, a decision-making model for new production processes based on the Genetic Simulated Annealing Algorithm (GSAA) is presented. The decision-making model considers not only the traditional factors, such as time, quality and cost, but also energy and resource consumption and environmental impact, which are different from the traditional methods. At the second level, a methodology is developed based on an IPO (Input-Process-Output) model that integrates assessments of resource consumption and environmental impact in terms of a materials balance principle for batch production processes. At the third level, based on the above two levels, a method for determining production processes that focus on low-carbon production is developed based on case-based reasoning, expert systems and feature technology for designing the process flow of a new component. Through the above three levels, a method for determining the production process to identify, quantify, assess, and optimize the production process with the goal of reducing and ultimately minimizing the environmental impact while maximizing the resource efficiency is effectively presented. The feasibility of the method is verified by a case study of a whole production process design at the above three levels.

Suggested Citation

  • Xian-Chun Tan & Yan-Yan Wang & Bai-He Gu & Ze-Kun Mu & Can Yang, 2011. "Improved Methods for Production Manufacturing Processes in Environmentally Benign Manufacturing," Energies, MDPI, vol. 4(9), pages 1-19, September.
  • Handle: RePEc:gam:jeners:v:4:y:2011:i:9:p:1391-1409:d:13951
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/4/9/1391/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/4/9/1391/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Feelders, A. J. & Daniels, H. A. M., 2001. "A general model for automated business diagnosis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 623-637, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Rosario Domingo & Marta María Marín & Juan Claver & Roque Calvo, 2015. "Selection of Cutting Inserts in Dry Machining for Reducing Energy Consumption and CO 2 Emissions," Energies, MDPI, vol. 8(11), pages 1-15, November.
    2. Shun Jia & Qinghe Yuan & Dawei Ren & Jingxiang Lv, 2017. "Energy Demand Modeling Methodology of Key State Transitions of Turning Processes," Energies, MDPI, vol. 10(4), pages 1-19, April.
    3. Alessandra Caggiano & Adelaide Marzano & Roberto Teti, 2016. "Sustainability Enhancement of a Turbine Vane Manufacturing Cell through Digital Simulation-Based Design," Energies, MDPI, vol. 9(10), pages 1-16, September.
    4. Wei Wei & Yile Liang & Feng Liu & Shengwei Mei & Fang Tian, 2014. "Taxing Strategies for Carbon Emissions: A Bilevel Optimization Approach," Energies, MDPI, vol. 7(4), pages 1-18, April.

    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. Caron, E.A.M. & Daniels, H.A.M., 2008. "Explanation of exceptional values in multi-dimensional business databases," European Journal of Operational Research, Elsevier, vol. 188(3), pages 884-897, August.
    2. H. A. M. Daniels & E. A. M. Caron, 2009. "Automated explanation of financial data," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 5-19, January.
    3. Nielsen Peter & Do Ngoc Anh Dung & Eriksen Thomas & Nielsen Izabela, 2014. "Towards an Analysis Methodology for Identifying Root Causes of Poor Delivery Performance," Foundations of Management, Sciendo, vol. 6(2), pages 31-42, December.

    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:4:y:2011:i:9:p:1391-1409:d:13951. 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.