IDEAS home Printed from https://ideas.repec.org/a/sae/engenv/v36y2025i3p1271-1289.html
   My bibliography  Save this article

Energy consumption optimization for sustainable flexible robotic cells: Proposing exact and metaheuristic methods

Author

Listed:
  • Mazyar Ghadiri Nejad
  • Reza Vatankhah Barenji
  • Güldal Güleryüz
  • Seyed Mahdi Shavarani

Abstract

Many manufacturing companies are always looking for a way to reduce energy consumption by utilizing energy-efficient production methods. These methods can be different depending on the type of products and production technology. For instance, one of the ways to increase energy efficiency and keep the precision of production is to use robots for the transportation of the parts among the machines and loading/unloading the machines. This technology is affordable compared to the technologies used in manufacturing companies. Manufacturing companies that rely on robotics technology must have a strategy to reduce energy costs and at the same time increase production by adjusting the intensity of processing or controlling the production rate. This study presents an exact solution method for flexible robotic cells to control the production rate and minimize energy consumption, which aims to both reduce electricity prices and minimize greenhouse gas (GHG) emissions under a lead time of production. Then, considering the NP-hardens nature of the problem, a heuristic solution method based on the genetic algorithm (GA) is proposed. Using the proposed approach, manufacturing companies will be able to make more accurate decisions about processing intensity and process scheduling while ensuring sustainability.

Suggested Citation

  • Mazyar Ghadiri Nejad & Reza Vatankhah Barenji & Güldal Güleryüz & Seyed Mahdi Shavarani, 2025. "Energy consumption optimization for sustainable flexible robotic cells: Proposing exact and metaheuristic methods," Energy & Environment, , vol. 36(3), pages 1271-1289, May.
  • Handle: RePEc:sae:engenv:v:36:y:2025:i:3:p:1271-1289
    DOI: 10.1177/0958305X231193868
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0958305X231193868
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0958305X231193868?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
    ---><---

    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:sae:engenv:v:36:y:2025:i:3:p:1271-1289. 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: SAGE Publications (email available below). General contact details of provider: .

    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.