Drum buffer rope-based heuristic for multi-level rolling horizon planning in mixed model production
Ullah Saif,
Zailin Guan,
Chuangjian Wang,
Cong He,
Lei Yue and
Jahanzaib Mirza
International Journal of Production Research, 2019, vol. 57, issue 12, 3864-3891
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.
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2019.1569272 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:57:y:2019:i:12:p:3864-3891
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20
DOI: 10.1080/00207543.2019.1569272
Access Statistics for this article
International Journal of Production Research is currently edited by Professor A. Dolgui
More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().