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Drum buffer rope-based heuristic for multi-level rolling horizon planning in mixed model production

Author

Listed:
  • Ullah Saif
  • Zailin Guan
  • Chuangjian Wang
  • Cong He
  • Lei Yue
  • Jahanzaib Mirza

Abstract

In recent years mixed model production industries are highly interested to apply Industry 4.0 and internet of things. The existing planning and scheduling methods are not efficient enough to make intelligent plans. Therefore, there is a strong need to develop planning and scheduling methods which can timely update the medium level and lower level schedules and can be utilised for Industry 4.0. Drum buffer rope (DBR) is a direct application of theory of constraint, is utilised here to make an efficient plan. Current research proposed a DBR-based heuristic algorithm (DBR-HA) for multi-level planning considering shifting bottleneck resource to make efficient schedule in rolling horizon in mixed model production environment and utilise capacity constraint resource (CCR) at maximum. The proposed DBR-HA identifies the drum, i.e. CCR, and make an efficient schedule on it in each lower level scheduling period and utilise a feedback method to update customer orders in each medium level planning horizon. The proposed method is useful to implement Industry 4.0 in mixed model industries and update their plan and schedule in real time. The performance of the proposed DBR-HA algorithm is measured and compared with the performance of the basic scheduling rules used in the Case Company based on a Case Company problem data. Results indicate that the proposed method is significant to reduce the gap between medium level planning and lower level schedules and gives an efficient medium level plan and lower level schedule in each planning horizon as compared to the other methods.

Suggested Citation

  • Ullah Saif & Zailin Guan & Chuangjian Wang & Cong He & Lei Yue & Jahanzaib Mirza, 2019. "Drum buffer rope-based heuristic for multi-level rolling horizon planning in mixed model production," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3864-3891, June.
  • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:12:p:3864-3891
    DOI: 10.1080/00207543.2019.1569272
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    Cited by:

    1. Kamble, Sachin S. & Gunasekaran, Angappa & Ghadge, Abhijeet & Raut, Rakesh, 2020. "A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation," International Journal of Production Economics, Elsevier, vol. 229(C).
    2. 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.

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