Particle swarm optimization based memetic algorithms framework for scheduling of central planned and distributed flowshops

Yixin Yang, Xiaoyi Feng, Bin Xin, Mengchen Ji, Xiying Du, Ling Wang, Hongjun Zhang, Bo Liu

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In this chapter, we provide a panorama of the PSO-based memetic algorithm (MA) for traditional permutation flowshop scheduling problem (PFSP) and its several variants. In the proposed algorithm, the global exploration ability of PSO and the local refinement ability of simulated annealing (SA) are delicately integrated and balanced. Some specific techniques related to the nature of PFSP are introduced to further improve the effectiveness of PSO-based MA. The key features in the proposed algorithm are detailed as follows. First, to apply PSO in solving combinatorial optimization problems such as PFSP, we rely on the ranked-order value (ROV) rule that uses random key representation to transform the continuous position information to scheduling permutations. Second, NEH and NEH-based constructive heuristics are introduced to guarantee a proportion of initial particles to be of good qualities. Third, to avoid the premature convergence problem of PSO, an adaptive SA-based local search is proposed to strengthen the exploitation in an efficient way. Forth, for the variation of PFSP that considers distributed processing factories, single assembly factory, and no-wait constraint (DAPFSP-NW), we include an extra encoding layer to represent the factory dispatch; thus, the proposed SA-based MA can still be applied. Moreover, the corresponding heuristic-based initialization and the neighborhoods adopted for local search are redefined. Last but not the least, for the variation with stochastic.

Original languageEnglish
Title of host publicationSwarm Intelligence - Volume 3
Subtitle of host publicationApplications
PublisherInstitution of Engineering and Technology
Pages463-494
Number of pages32
ISBN (Electronic)9781785616310
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • Assembling
  • Combinatorial mathematics
  • Convergence
  • Flow shop scheduling
  • Particle swarm optimisation
  • Search problems
  • Simulated annealing
  • Stochastic processes

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