IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i6p2272-d332432.html
   My bibliography  Save this article

Industry 4.0-Based Real-Time Scheduling and Dispatching in Lean Manufacturing Systems

Author

Listed:
  • Muawia Ramadan

    (Department of Industrial Engineering, Faculty of Engineering and Information Technology, An-Najah National University, P.O. Box 7, Nablus, West Bank, Palestine)

  • Bashir Salah

    (Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia)

  • Mohammed Othman

    (Department of Industrial Engineering, Faculty of Engineering and Information Technology, An-Najah National University, P.O. Box 7, Nablus, West Bank, Palestine)

  • Arsath Abbasali Ayubali

    (Department of Mechanical Engineering, College of Engineering Guindy, Anna University, Chennai 600025, India)

Abstract

Lean manufacturing is one of the most popular improvement agents in the pursuit of perfection. However, in today’s complex and dynamic manufacturing environments, lean tools are facing an inevitable death. Industry 4.0 can be integrated with lean tools to avoid their end. Therefore, the primary purpose of this paper is to introduce an Industry 4.0-based lean framework called dynamic value stream mapping (DVSM) to digitalize lean manufacturing through the integration of lean tools and Industry 4.0 technologies. DVSM with its powerful features is proposed to be the smart IT platform that can sustain lean tools and keep them alive and effective. This paper specifically tackles the scheduling and dispatching in today’s lean manufacturing environments, where the aim of this research is developing a smart lean-based production scheduling and dispatching model to achieve the lean target through optimizing the flow along the VSM and minimizing the manufacturing lead time. The developed model, called the real-time scheduling and dispatching module (RT-SDM), runs on DVSM. The RT-SDM is represented through a mathematical model using mixed integer programming. Part of the testing and verification process, a simplified IT-based software, has been developed and applied on a smart factory lab.

Suggested Citation

  • Muawia Ramadan & Bashir Salah & Mohammed Othman & Arsath Abbasali Ayubali, 2020. "Industry 4.0-Based Real-Time Scheduling and Dispatching in Lean Manufacturing Systems," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2272-:d:332432
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/6/2272/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/6/2272/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bashir Salah & Mustufa Haider Abidi & Syed Hammad Mian & Mohammed Krid & Hisham Alkhalefah & Ali Abdo, 2019. "Virtual Reality-Based Engineering Education to Enhance Manufacturing Sustainability in Industry 4.0," Sustainability, MDPI, vol. 11(5), pages 1-19, March.
    2. Jonghyuk Kim & Hyunwoo Hwangbo, 2019. "Real-Time Early Warning System for Sustainable and Intelligent Plastic Film Manufacturing," Sustainability, MDPI, vol. 11(5), pages 1-13, March.
    3. Chongwatpol, Jongsawas & Sharda, Ramesh, 2013. "RFID-enabled track and traceability in job-shop scheduling environment," European Journal of Operational Research, Elsevier, vol. 227(3), pages 453-463.
    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. Shuting Wang & Jie Meng & Yuanlong Xie & Liquan Jiang & Han Ding & Xinyu Shao, 2023. "Reference training system for intelligent manufacturing talent education: platform construction and curriculum development," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1125-1164, March.
    2. Srivastava, Deepak Kumar & Kumar, Vikas & Ekren, Banu Yetkin & Upadhyay, Arvind & Tyagi, Mrinal & Kumari, Archana, 2022. "Adopting Industry 4.0 by leveraging organisational factors," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    3. Jiewu Leng & Pingyu Jiang, 2019. "Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 979-994, March.
    4. Zhong, Ray Y. & Huang, George Q. & Lan, Shulin & Dai, Q.Y. & Chen, Xu & Zhang, T., 2015. "A big data approach for logistics trajectory discovery from RFID-enabled production data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 260-272.
    5. Elsa Chaerun Nisa & Yean-Der Kuan, 2021. "Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    6. Guo, Z.X. & Ngai, E.W.T. & Yang, Can & Liang, Xuedong, 2015. "An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment," International Journal of Production Economics, Elsevier, vol. 159(C), pages 16-28.
    7. Montecchi, Matteo & Plangger, Kirk & West, Douglas C., 2021. "Supply chain transparency: A bibliometric review and research agenda," International Journal of Production Economics, Elsevier, vol. 238(C).
    8. Guillermo Fuertes & Jorge Zamorano & Miguel Alfaro & Manuel Vargas & Jorge Sabattin & Claudia Duran & Rodrigo Ternero & Ricardo Rivera, 2022. "Opportunities of the Technological Trends Linked to Industry 4.0 for Achieve Sustainable Manufacturing Objectives," Sustainability, MDPI, vol. 14(18), pages 1-36, September.
    9. Lili Wang & Bin Hu & Yihang Feng & Yanting Duan & Wuyi Zhang, 2022. "Food supply network disruption and mitigation: an integrated perspective of traceability technology and network structure," Computational and Mathematical Organization Theory, Springer, vol. 28(4), pages 352-389, December.
    10. Niraj Kumar Vishvakarma & Rohit Kumar Singh & R. R. K. Sharma, 2022. "Cluster and DEMATEL Analysis of Key RFID Implementation Factors Across Different Organizational Strategies," Global Business Review, International Management Institute, vol. 23(1), pages 176-191, February.
    11. Bernard Bińczycki & Sławomir Dorocki, 2022. "Industry 4.0: A Chance or a Threat for Gen Z? The Perspective of Economics Students," Sustainability, MDPI, vol. 14(14), pages 1-13, July.
    12. Macaria Hernández-Chávez & José M. Cortés-Caballero & Ángel A. Pérez-Martínez & Luis F. Hernández-Quintanar & Karen Roa-Tort & Josué D. Rivera-Fernández & Diego A. Fabila-Bustos, 2021. "Development of Virtual Reality Automotive Lab for Training in Engineering Students," Sustainability, MDPI, vol. 13(17), pages 1-17, August.
    13. Yingfeng Zhang & Dong Xi & Haidong Yang & Fei Tao & Zhe Wang, 2019. "Cloud manufacturing based service encapsulation and optimal configuration method for injection molding machine," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2681-2699, October.
    14. Jihyung Kim & Kyeong-sun Kim & Jonghyeon Ka & Wooksung Kim, 2023. "Teaching Methodology for Understanding Virtual Reality and Application Development in Engineering Major," Sustainability, MDPI, vol. 15(3), pages 1-22, February.
    15. Athar Ajaz Khan & János Abonyi, 2022. "Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy," Sustainability, MDPI, vol. 14(15), pages 1-40, August.
    16. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
    17. Chongwatpol, Jongsawas, 2015. "Integration of RFID and business analytics for trade show exhibitors," European Journal of Operational Research, Elsevier, vol. 244(2), pages 662-673.
    18. Ahmed Musa & Al-Amin Abba Dabo, 2016. "A Review of RFID in Supply Chain Management: 2000–2015," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 17(2), pages 189-228, June.
    19. Andrzej Paszkiewicz & Mateusz Salach & Paweł Dymora & Marek Bolanowski & Grzegorz Budzik & Przemysław Kubiak, 2021. "Methodology of Implementing Virtual Reality in Education for Industry 4.0," Sustainability, MDPI, vol. 13(9), pages 1-25, April.
    20. Marcelo Royo-Vela & Grzegorz Leszczyński & Mariell Velasquez-Serrano, 2022. "Sustainable Value Co-Production and Co-Creation in Virtual Reality: An Exploratory Research on Business-to-Business Interactions," Sustainability, MDPI, vol. 14(13), pages 1-16, June.

    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:jsusta:v:12:y:2020:i:6:p:2272-:d:332432. 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.