Application of Improved Sparrow Search Algorithm to Flexible Job Shop Scheduling Problem

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Long-Yan Xu, Yi-Fan Zhao, Peng Li1, Ming Li, Ya-Hong Zhai, Li-Ming Huang


When the reality of energy saving and reduction of enterprises’ emissions are a concern, research on the investigation of establishing mathematical models to optimize the production time has been studied. In this article, an enhanced sparrow optimization method is proposed. First, two-layer coding is employed for workpieces and machines according to the model requirements. Secondly, the three-dimensional chaotic mapping scheme is presented to improve the population heterogeneity of the algorithm, and the adaptive inertia weight balance algorithm is implemented to offset the speed of the convergence and its probability. Finally, the Cauchy mutation scheme is adopted to help the algorithm jump out of the local optimum. Simulated data is run to check the superiority of the proposed method. So, through the simulations and comparisons of 10 kinds of test datasets, the outcomes suggest that the solution quality of the enhanced sparrow optimization method has been effectively advanced, and its good global optimization ability is shown, which can provide scheduling strategies for workshop productions. One of the successes of the ISSA algorithm is its superior search accuracy.

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