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Genetic Programming with Tabu List for Dynamic Flexible Job Shop Scheduling.

Fangfang Zhang1, Mazhar Ansari Ardeh2, Yi Mei3

  • 1Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand fangfang.zhang@ecs.vuw.ac.nz.

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|May 14, 2025
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Summary
This summary is machine-generated.

This study introduces a genetic programming (GP) algorithm with tabu lists to enhance exploration for dynamic flexible job shop scheduling (DFJSS). The improved GP effectively maintains population diversity and discovers better scheduling heuristics.

Keywords:
Genetic programmingdynamic flexible job shop schedulingexploration abilityscheduling heuristicstabu list

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Computer Science

Background:

  • Dynamic flexible job shop scheduling (DFJSS) is a complex combinatorial optimization problem.
  • Genetic programming (GP) is a common hyper-heuristic for DFJSS but suffers from rapid diversity loss.
  • Weak exploration ability limits GP's effectiveness in finding optimal scheduling heuristics.

Purpose of the Study:

  • To propose an effective GP algorithm with tabu lists to enhance exploration for DFJSS.
  • To improve the exploration ability of GP by guiding it to unexplored areas.
  • To enhance the overall effectiveness of GP for solving DFJSS problems.

Main Methods:

  • Utilized phenotypic characterization to represent GP individuals as vectors for DFJSS.
  • Developed tabu lists to store phenotypic characterizations of explored individuals.
  • Implemented a mechanism to discard offspring if their phenotypic characterizations are found in tabu lists, promoting exploration of unseen solutions.

Main Results:

  • The proposed GP algorithm with tabu lists outperformed compared algorithms in most tested scenarios.
  • The algorithm successfully maintained a diverse and well-distributed population throughout the evolutionary process.
  • Demonstrated that the algorithm explores a larger search space to identify effective scheduling heuristics.

Conclusions:

  • The proposed GP algorithm with tabu lists is effective in enhancing exploration for DFJSS.
  • The method improves population diversity and leads to the discovery of superior scheduling heuristics.
  • This approach offers a promising direction for improving hyper-heuristic performance in dynamic scheduling environments.