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Related Experiment Videos

An effective PSO-based memetic algorithm for flow shop scheduling.

Bo Liu1, Ling Wang, Yi-Hui Jin

  • 1Institute of Process Control, Department of Automation, Tsinghua University, Beijing 100084, China. liub01@mails.tsinghua.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2007
PubMed
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This study introduces a Particle Swarm Optimization-based Memetic Algorithm (PSOMA) to solve the complex permutation flow shop scheduling problem, aiming to minimize maximum completion time. The novel PSOMA effectively balances exploration and exploitation for improved scheduling optimization.

Area of Science:

  • Operations Research
  • Computer Science
  • Artificial Intelligence

Background:

  • The permutation flow shop scheduling problem (PFSSP) is a critical NP-hard combinatorial optimization challenge.
  • Minimizing the maximum completion time (makespan) is a primary objective in PFSSP.
  • Existing methods often struggle to balance exploration and exploitation for optimal solutions.

Purpose of the Study:

  • To propose an effective Particle Swarm Optimization (PSO)-based Memetic Algorithm (PSOMA) for the PFSSP.
  • To minimize the maximum completion time in permutation flow shop environments.
  • To enhance optimization performance by balancing exploration and exploitation capabilities.

Main Methods:

  • A novel PSO-based Memetic Algorithm (PSOMA) integrating PSO's evolutionary search with specialized local search operators.

Related Experiment Videos

  • A ranked-order value rule using random key representation to map continuous PSO values to job permutations for PFSSP.
  • Incorporation of the Nawaz-Enscore-Ham (NEH) heuristic for high-quality initial population generation.
  • Probabilistic application of NEH_1 insertion and Simulated Annealing (SA)-based local search with adaptive meta-Lamarckian learning.
  • Pairwise-based local search for enhanced exploitation.

    • Main Results:

    • The proposed PSOMA demonstrates significant effectiveness in solving the PFSSP benchmarks.
    • The algorithm successfully balances exploration and exploitation through its hybrid search strategy.
    • Simulation results validate the superiority of PSOMA over existing approaches for makespan minimization.

    Conclusions:

    • The PSOMA is an effective and robust approach for the permutation flow shop scheduling problem.
    • The integration of PSO with diverse local search strategies enhances optimization performance.
    • The study provides valuable insights into parameter tuning for improved scheduling outcomes.