Coordinated scheduling of the transfer lots in an assembly-type supply chain: a genetic algorithm approach
Tülin İnkaya () and
Mehmet Akansel
Additional contact information
Tülin İnkaya: Uludağ University
Mehmet Akansel: Uludağ University
Journal of Intelligent Manufacturing, 2017, vol. 28, issue 4, No 11, 1005-1015
Abstract:
Abstract In this study, we consider coordinated scheduling of the transfer lots in an assembly-type supply chain. An assembly-type supply chain consists of at least two stages, where the upstream stages manufacture the components for several products to be assembled at the downstream stages. In order to enable faster flow of products through the supply chain and to decrease the work-in-process inventory, the concept of lot streaming is used as a means of supply chain coordination. We introduce a mathematical model, which finds the optimal transfer lot sizes in the supply chain. The objective is the minimization of the sum of weighted flow and inventory costs. We develop genetic algorithm (GA) based heuristics to solve the proposed model efficiently. The experimental results show that the proposed GA-based approaches provide acceptable results in reasonable amount of time. We also show that coordination with lot streaming provides improvements in the supply chain performance.
Keywords: Lot streaming; Supply chain coordination; Assembly-type supply chain; Genetic algorithm (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://link.springer.com/10.1007/s10845-015-1041-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
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:spr:joinma:v:28:y:2017:i:4:d:10.1007_s10845-015-1041-9
Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845
DOI: 10.1007/s10845-015-1041-9
Access Statistics for this article
Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak
More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().