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Dynamic quadratic decomposition-based evolutionary algorithm for multi-objective fuzzy flexible jobshop scheduling
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AI
DOI：10.1016/j.swevo.2025.101884.png)
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Abstract
En 中文
Multi-objective Fuzzy Flexible Jobshop Scheduling Problems (MFFJSPs) have garnered widespread attention since they are able to handle the uncertainty of processing time in actual production. Nevertheless, making a good balance between the diversity and convergence of non-dominated solutions is a challenging issue that cannot be overlooked when MFFJSP is solved. To deal with these issues, this work proposes a Dynamic Quadratic Decomposition-based Multi-objective Evolutionary Algorithm (DQD-MOEA) to solve MFFJSP by minimizing makespan and total machine workload. To solve a problem that the distribution and diversity of searched non-dominant solutions are poor due to the discrete decision space and objective space of MFFJSP, it proposes a dynamic quadratic decomposition method. Its core idea is to eliminate all the failed reference vectors because they have no intersection with areal Pareto front, and ensure that solutions evolve along effective reference vectors. This work also introduces a problem-specific local search method to accelerate the solution convergence for MFFJSP. It proposes a hybrid initialization method to improve the quality of initial solutions. Finally, a series of experiments are performed and the results demonstrate that DQD-MOEA is significantly better than state-of-the-art algorithms in terms of convergence and solution diversity when solving widely-tested benchmark cases.
Keywords:
Multi-objective evolutionary algorithm
Flexible jobshop scheduling
Dynamic quadratic decomposition
Fuzzy processing time
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