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Improved genetic algorithm for solving the fuzzy multiobjective Job Shop problem

机译:改进的遗传算法求解模糊多目标Job Shop问题

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This paper studies the influence of the encoding and decoding on the result of the Job Shop problem under E/T indicators and improve the coding methods to make the optimal object span in order to adapt to different delivery windows earliness / tardiness scheduling problem. In this paper, The trapezoidal fuzzy number which has more representation as flexible operating processing time under fuzzy environment was used. Multi-attribute decision making method based on possibility was used. In this way it can reduce the intermediate process, avoid the loss of information, and enhance the effectiveness of fuzzy evaluation. Simulation results verify the effectiveness of the algorithm.
机译:本文研究了在E / T指标下编码和解码对Job Shop问题结果的影响,并改进了编码方法以使对象跨度最佳,以适应不同的交付窗口提前/拖后调度问题。本文使用梯形模糊数,它在模糊环境下具有更灵活的运算处理时间,因此具有更多的表示形式。使用了基于可能性的多属性决策方法。这样可以减少中间过程,避免信息丢失,提高模糊评价的有效性。仿真结果验证了该算法的有效性。

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