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A new adaptive neural network and heuristics hybrid approach for job-shop scheduling

机译:作业车间调度的新的自适应神经网络和启发式混合方法

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摘要

A new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving the feasible solution. Two heuristics are presented, which can be combined with the neural network. One heuristic is used to accelerate the solving process of the neural network and guarantee its convergence, the other heuristic is used to obtain non-delay schedules from the feasible solutions gained by the neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency. The strategy for solving practical job-shop scheduling problems is provided.
机译:提出了一种用于作业车间调度的新的自适应神经网络和启发式混合方法。神经网络具有在解决可行解的同时调整其连接权重和神经单元偏差的特性。提出了两种启发式方法,可以与神经网络结合使用。一种启发式方法用于加速神经网络的求解过程并保证其收敛性,另一种启发式方法用于从神经网络获得的可行解中获取非延迟调度。计算机仿真表明,提出的混合方法具有很高的速度和效率。提供了解决实际作业车间调度问题的策略。

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