An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem

Maroua Nouiri, Abdelghani Bekrar (), Abderezak Jemai, Smail Niar and Ahmed Chiheb Ammari
Additional contact information
Maroua Nouiri: Faculty of Science of Tunis
Abdelghani Bekrar: UVHC, LAMIH Laboratory
Abderezak Jemai: Faculty of Science of Tunis
Smail Niar: UVHC, LAMIH Laboratory
Ahmed Chiheb Ammari: Carthage University

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 3, No 10, 603-615

Abstract: Abstract Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partial FJSP and Total FJSP, are tested. Experimental results proved that the developed PSO is enough effective and efficient to solve the FJSP. Our other objective in this paper, is to study the distribution of the PSO-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events. For this aim, two multi-agent based approaches are proposed and compared using different benchmark instances.

Keywords: Flexible job-shop scheduling problem; Makespan; Particle swarm optimization algorithm; Routing and scheduling; Multi-agent system; Enmbeded system (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-015-1039-3 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:3:d:10.1007_s10845-015-1039-3

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

DOI: 10.1007/s10845-015-1039-3

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 ().