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A multipopulation PSO based memetic algorithm for permutation flow shop scheduling.

Ruochen Liu1, Chenlin Ma1, Wenping Ma1

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Summary
This summary is machine-generated.

This study introduces a novel multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) to solve the complex permutation flow shop scheduling problem (PFSSP). MPSOMA demonstrates superior performance compared to existing methods for production scheduling optimization.

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Area of Science:

  • Operations Research
  • Computer Science
  • Artificial Intelligence

Background:

  • The permutation flow shop scheduling problem (PFSSP) is a computationally challenging combinatorial optimization problem.
  • Effective solutions are crucial for optimizing production scheduling and manufacturing efficiency.

Purpose of the Study:

  • To propose a novel and effective algorithm for solving the permutation flow shop scheduling problem.
  • To enhance the performance of particle swarm optimization (PSO) through a multipopulation memetic approach.

Main Methods:

  • A multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is developed.
  • The algorithm divides populations into subpopulations, employing variable neighborhood search (VNS) and individual improvement schemes (IIS).
  • Estimation of distribution algorithm (EDA) is used to construct probabilistic models for updating individuals.

Main Results:

  • The proposed MPSOMA algorithm was compared against PSO-based memetic algorithm (PSOMA) and hybrid PSO with EDA (PSOEDA).
  • Experiments were conducted on 29 benchmark PFSSP instances from the OR-library.
  • MPSOMA achieved superior results, demonstrating its effectiveness for the PFFSP.

Conclusions:

  • The developed MPSOMA is an effective approach for addressing the permutation flow shop scheduling problem.
  • The hybrid strategy combining multipopulation PSO, memetic local search, and EDA offers significant advantages.
  • This research contributes to the field of production scheduling optimization.