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An effective hybrid particle swarm optimization for no-wait flow shop scheduling

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Abstract

The no-wait flow shop scheduling that requires jobs to be processed without interruption between consecutive machines is a typical NP-hard combinatorial optimization problem, and represents an important area in production scheduling. This paper proposes an effective hybrid algorithm based on particle swarm optimization (PSO) for no-wait flow shop scheduling with the criterion to minimize the maximum completion time (makespan). In the algorithm, a novel encoding scheme based on random key representation is developed, and an efficient population initialization, an effective local search based on the Nawaz-Enscore-Ham (NEH) heuristic, as well as a local search based on simulated annealing (SA) with an adaptive meta-Lamarckian learning strategy are proposed and incorporated into PSO. Simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed hybrid algorithm.

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Authors and Affiliations

  1. Institute of Process Control, Department of Automation, Tsinghua University, Beijing, 100084, People’s Republic of China

    Bo Liu, Ling Wang & Yi-Hui Jin

Authors
  1. Bo Liu
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  2. Ling Wang
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  3. Yi-Hui Jin
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Correspondence to Bo Liu.

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Liu, B., Wang, L. & Jin, YH. An effective hybrid particle swarm optimization for no-wait flow shop scheduling. Int J Adv Manuf Technol 31, 1001–1011 (2007). https://doi.org/10.1007/s00170-005-0277-5

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  • DOI: https://doi.org/10.1007/s00170-005-0277-5

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