Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art

Mosahar Tarimoradi (), M. H. Fazel Zarandi (), Hosain Zaman () and I. B. Turksan ()
Additional contact information
Mosahar Tarimoradi: Amirkabir University of Technology
M. H. Fazel Zarandi: Amirkabir University of Technology
Hosain Zaman: HEC Montréal
I. B. Turksan: University of Toronto

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 7, No 5, 1579 pages

Abstract: Abstract Supply chain network designing and programming is a momentous issue that many practitioners have focused on and contributed numerous novelties for this prompt. This paper puts forward a fuzzy multi-agent system according to which compatible with the decision makers’ interests and environmental survey, identifies the parameters of the mathematical model. An embedded optimization party including evolutionary-based optimizer intelligent agents, obtains non-dominated potential solutions. The output of these optimizer agents during the calibration process is an underpinning for evaluating the performance of the party. The system makes the policy of optimization complying with the results evaluation as well as the decision makers’ elaborated desires. Afterwards, in step with this policy, it sets a pool from obtained Pareto Fronts and aggregates them to extract a set of the best individuals. It interactively represents this set to the decision makers and catches their desired circumstance amongst these optional solutions. Proposing the network graph and program—which its generic morphography is determined—for decision makers is contrived as the system last stage. The main competencies of this system could be contemplated regarding the facts that it interactively fulfills the decision makers’ utilities relying on its robustness in optimization, self-tuning, training loop, ambient intelligence and consciousness toward the changes in environment.

Keywords: Agent-based optimization; Fuzzy inference; Supply chain network; Performance evaluation and approximation (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-015-1170-1 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:7:d:10.1007_s10845-015-1170-1

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

DOI: 10.1007/s10845-015-1170-1

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 ().