Buffer allocation in a flow shop with capacitated batch transports

Ai-Lin Yu, Hui-Yu Zhang, Qing-Xin Chen, Ning Mao and Shao-Hui Xi

Journal of the Operational Research Society, 2022, vol. 73, issue 4, 888-904

Abstract: Automated material handling systems play a crucial role in intelligent manufacturing workshops. The main difficulty in optimising buffer allocation in such systems is to provide good evaluation for the performance measures of interest, such as throughput, and cycle time, for customised manufacturing systems. However, existing research lacks an in-depth consideration of the integration of production and material handling systems. Therefore, a flow shop with capacitated batch transports, where the transported batch size depends on the number of jobs in buffers and capacity of transporters, is presented. The system is modelled as an open queueing network with blocking, and an approximation method is proposed for computing the performance measures. Then, an iterative optimisation algorithm embedded with the performance evaluation method is developed to determine an optimal buffer allocation. Finally, the computational experiments are conducted to validate the accuracy and efficiency of the proposed approaches.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2020.1866957 (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:tjorxx:v:73:y:2022:i:4:p:888-904

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2020.1866957

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().