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Discrete bat algorithm for optimal problem of permutation flow shop scheduling.

Qifang Luo1, Yongquan Zhou1, Jian Xie1

  • 1College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, Guangxi 530006, China.

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Summary
This summary is machine-generated.

A new discrete bat algorithm (DBA) effectively solves the optimal permutation flow shop scheduling problem (PFSP). This method enhances performance by dividing problems and integrating neighborhood search for efficient scheduling solutions.

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Area of Science:

  • Operations Research
  • Computer Science
  • Artificial Intelligence

Background:

  • The permutation flow shop scheduling problem (PFSP) is a complex combinatorial optimization challenge.
  • Existing algorithms may struggle with scalability and efficiency for large-scale PFSP instances.

Purpose of the Study:

  • To propose a novel discrete bat algorithm (DBA) for solving the optimal permutation flow shop scheduling problem (PFSP).
  • To enhance the performance and efficiency of bat algorithms for complex scheduling tasks.

Main Methods:

  • A discrete bat algorithm (DBA) was developed by adapting the basic bat algorithm.
  • The PFSP was decomposed into sub-scheduling problems solved using the NEH heuristic.
  • Probabilistic subsequence operations were incorporated into pulse emission and loudness adjustments.
  • An intensive virtual population neighborhood search was integrated to refine solutions.

Main Results:

  • The discrete bat algorithm demonstrated suitability for the optimal permutation flow shop scheduling problem.
  • Experimental results confirmed the efficiency of the proposed DBA in solving PFSP.

Conclusions:

  • The developed discrete bat algorithm offers an effective approach for optimal permutation flow shop scheduling.
  • The integration of heuristic methods and neighborhood search significantly improves scheduling efficiency.