Abstract
Targeting the characteristics of performance degradation caused by prolonged machine operation in real production, this article examines the flexible job shop scheduling problem under machine deterioration effects, develops the FJSP-MDE mathematical model to minimize makespan, and presents an Improved Whale Optimization Algorithm (IWOA) to solve the problem. The algorithm employs a hybrid population initialization strategy to generate high-quality initial solutions during the initialization phase. Nonlinear convergence factors and inertia weighting strategies are designed to balance global search and local exploitation capabilities. Stochastic differential variance and golden sinusoidal strategies are designed to enhance population diversity and expand the search range. Finally, the feasibility and effectiveness of the IWOA algorithm are proved by simulation experiments.
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Data will be made available on request and it is provided by the corresponding author.
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Acknowledgements
The authors are very grateful to the anonymous reviewers for their insightful comments and helpful suggestions.This work was supported by Natural Science Foundation of China (Grants 61403277), and Humanities and Social Science Research Projects for Colleges in Tianjin (Grants 20132151).
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Kun Li: framework construction, methodological guidance, supervision; Silong Tian: modelling, derivation, experimentation, writing.
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Li, K., Tian, S. An improved whale optimization algorithm for flexible job shop scheduling problems with machine deterioration effects. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44409-4
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DOI: https://doi.org/10.1038/s41598-026-44409-4
