Modeling and measuring the structural complexity in assembly supply chain networks

Nima Hamta, M. Akbarpour Shirazi (), Sara Behdad and S.M.T. Fatemi Ghomi
Additional contact information
Nima Hamta: Amirkabir University of Technology (Tehran Polytechnic)
M. Akbarpour Shirazi: Amirkabir University of Technology (Tehran Polytechnic)
Sara Behdad: The State University of New York
S.M.T. Fatemi Ghomi: Amirkabir University of Technology (Tehran Polytechnic)

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 2, No 1, 259-275

Abstract: Abstract Complexity of assembly supply chains (ASCs) is a challenge for designers and managers, especially when ASC systems become increasingly complex due to technological developments and geographically various sourcing arrangements. One of the major challenges at the early design stage is to make decision about an appropriate configuration of ASC. This paper addresses modeling and measuring the structural complexity of ASC networks in order to establish a framework obtaining the optimal ASC configuration. Considering relationship between supply chains and assembly systems, structural complexity measures for ASC network and assembly lines inside the network are developed based on Shannon’s information entropy. This complexity model can be used to configure supply chain networks and assembly systems with robust performance. In order to generate different feasible configurations of ASCs, a four-step algorithm is proposed considering assembly sequence constraint. Finally, the optimal ASC network is obtained by comparing the total complexity values of the feasible configurations.

Keywords: Structural complexity; Assembly supply chain network; Assembly line; Optimal configuration (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-015-1106-9 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:29:y:2018:i:2:d:10.1007_s10845-015-1106-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-015-1106-9

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 ().