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Lagrangian relaxation neural networks for job shop scheduling

机译:拉格朗日松弛神经网络​​用于作业车间调度

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Manufacturing scheduling is an important but difficult task. In order to effectively solve such combinatorial optimization problems, the paper presents a Lagrangian relaxation neural network (LRNN) for separable optimization problems by combining recurrent neural network optimization ideas with Lagrangian relaxation (LR) for constraint handling. The convergence of the network is proved, and a general framework for neural implementation is established, allowing creative variations. When applying the network to job shop scheduling, the separability of problem formulation is fully exploited, and a new neuron-based dynamic programming is developed making innovative use of the subproblem structure. Testing results obtained by software simulation demonstrate that the method is able to provide near-optimal solutions for practical job shop scheduling problems, and the results are superior to what have been reported in the neural network scheduling literature. In fact, the digital implementation of LRNN for job shop scheduling is similar to the traditional LR approaches. The method, however, has the potential to be implemented in hardware with much improved quality and speed.
机译:制造调度是一项重要但艰巨的任务。为了有效解决此类组合优化问题,通过结合递归神经网络优化思想与拉格朗日松弛(LR)进行约束处理,提出了可分离优化问题的拉格朗日松弛神经网络​​(LRNN)。证明了网络的收敛性,并且建立了神经实施的通用框架,允许进行创造性的变化。当将网络应用于车间调度时,充分利用了问题表述的可分离性,并且开发了一种新的基于神经元的动态规划方法,并利用了子问题结构的创新性。通过软件仿真获得的测试结果表明,该方法能够为实际的作业车间调度问题提供接近最佳的解决方案,并且其结果优于神经网络调度文献中的报道。实际上,用于作业车间调度的LRNN的数字实现类似于传统的LR方法。然而,该方法具有以大大提高的质量和速度在硬件中实现的潜力。

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