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A multiobjective optimizer with a K-means cluster algorithm for a distributed flexible flowshop rescheduling problem
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DOI：10.1016/j.asoc.2024.112124.png)
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A distributed flexible flowshop problem (DFFSP) has been extensively studied over recent years. It is assumed that all jobs to be processed are exactly known in advance, and machines are able to work continuously. In practice, however, new jobs often arrive suddenly. Machines sometimes break down unexpectedly. These lower the performance of the scheduling generated, even make it infeasible. To address this problem, this paper considers a rescheduling DFFSP (DFFRP) with new job arrivals and machine breakdowns. The objective is to minimize makespan and the robustness metrics at the same time. Firstly, we propose a multi-objective mixedinteger linear programming model and a non-dominated sorting genetic algorithm-II based on K-means clustering algorithm (KNSGA-II). Secondly, the problem-specific knowledge is explored and a speed-up strategy is designed to save the algorithmic computation. Thirdly, an initialization strategy based on K-means clustering algorithm is developed to generate high-quality initial solutions. And a novel crossover and mutation operator is employed to accelerate the convergence of the algorithm. Finally, by comparing with a number of advanced multi-objective algorithms in the literature in comprehensive experiments, the proposed algorithm has been demonstrated to be much more effective for solving the DFFRP under consideration.
Keyword:
Distributed flexible flowshop
Multiobjective
Rescheduling
New job insertion
Machine breakdown
K-means
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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