Solving Flexible Job-shop Scheduling Problem Based on Improved Genetic Algorithm

DOI

10.2991/978-94-6463-736-6_26How to use a DOI?

Keywords

Job shop scheduling; Flexibility; Genetic algorithm; Self-tuning the search domain

Abstract

In order to optimize the allocation of production factors and improve resource utilization, this paper constructs a scheduling model aiming at minimizing the maximum completion time, and proposes an improved genetic algorithm with self-adjusting search domain to solve flexible job-shop scheduling problems. The algorithm adopts a double-layer chromosome coding scheme based on sequence arrangement and machine selection. A new population initialization method was designed, and adaptive parameters were introduced to optimize the genetic operation flow in the crossover and mutation stages. The introduction of new populations in the later stages of evolution increases the diversity of chromosomes, and self-adjusts the algorithm search domain to help the population jump out of the local optimal to find a better global solution. The experimental results show that the proposed improved genetic algorithm performs well in terms of optimization accuracy and convergence ability.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xinhui Zhou
PY  - 2025
DA  - 2025/05/22
TI  - Solving Flexible Job-shop Scheduling Problem Based on Improved Genetic Algorithm
BT  - Proceedings of the 2025 4th International Conference on Engineering Management and Information Science (EMIS 2025)
PB  - Atlantis Press
SP  - 224
EP  - 231
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-736-6_26
DO  - 10.2991/978-94-6463-736-6_26
ID  - Zhou2025
ER  -