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Swarm Intelligence

, Volume 13, Issue 2, pp 145–168 | Cite as

A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem

  • Rim ZarroukEmail author
  • Imed Eddine Bennour
  • Abderrazek Jemai
Article
  • 125 Downloads

Abstract

Particle swarm optimization is a population-based stochastic algorithm designed to solve difficult optimization problems, such as the flexible job shop scheduling problem. This problem consists of scheduling a set of operations on a set of machines while minimizing a certain objective function. This paper presents a two-level particle swarm optimization algorithm for the flexible job shop scheduling problem. The upper level handles the operations-to-machines mapping, while the lower level handles the ordering of operations on machines. A lower bound-checking strategy on the optimal objective function value is used to reduce the number of visited solutions and the number of objective function evaluations. The algorithm is benchmarked against existing state-of-the-art algorithms for the flexible job shop scheduling problem on a significant number of diverse benchmark problems.

Keywords

Flexible job shop Scheduling Particle swarm Optimization 

Notes

Supplementary material

11721_2019_167_MOESM1_ESM.pdf (42 kb)
Supplementary material 1 (pdf 41 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Polytechnic SchoolUniversity of CarthageTunisTunisia
  2. 2.NOCCS Laboratory, National Engineering School (ENISO)University of SousseSousseTunisia
  3. 3.SERCOM Laboratory, Polytechnic SchoolUniversity of Carthage, INSATTunisTunisia

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