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An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP

Software Technology Institute, Dalian Jiaotong University, Dalian 116021, China
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Algorithms 2019, 12(11), 243; https://doi.org/10.3390/a12110243
Received: 23 October 2019 / Revised: 12 November 2019 / Accepted: 12 November 2019 / Published: 14 November 2019
For solving the complex flexible job-shop scheduling problem, an improved genetic algorithm with adaptive variable neighborhood search (IGA-AVNS) is proposed. The improved genetic algorithm first uses a hybrid method combining operation sequence (OS) random selection with machine assignment (MA) hybrid method selection to generate the initial population, and it then groups the population. Each group uses an improved genetic operation for global search, then the better solutions from each group are stored in the elite library, and finally, the adaptive local neighborhood search is used in the elite library for detailed local searches. The simulation experiments are carried out by three sets of international standard examples. The experimental results show that the IGA-AVNS algorithm is an effective algorithm for solving flexible job-shop scheduling problems. View Full-Text
Keywords: adaptive; flexible job-shop scheduling problem; improved genetic algorithm; variable neighborhood search adaptive; flexible job-shop scheduling problem; improved genetic algorithm; variable neighborhood search
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Gu, X.; Huang, M.; Liang, X. An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP. Algorithms 2019, 12, 243.

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