A Coupled Transiently Chaotic Neural Network Approach for Identical Parallel Machine Scheduling

在相同机器上安排工作是经常在各种各样的生产系统遇到的一种状况。在这份报纸，一新联合了短暂地混乱的神经网络(CTCNN ) 被提出解决相同平行机器安排。这个问题的一个混合整数编程模型被介绍一个排列矩阵表达式转变成 CTCNN 计算体系结构。新计算精力功能被建议除所有限制以外表示目的。特别地，在精力功能在惩罚术语之中存在的折衷问题被使用变化时间的惩罚参数克服。最后，结果与 100 个随机的起始的条件在 3 个不同规模问题上测试了证明网络收敛并且能在合理时间解决这些问题。

Scheduling jobs on identical machines is a situation frequently encountered in various manufacturing systems. In this paper, a new coupled transiently chaotic neural network （CTCNN） is put forward to solve identical parallel machine scheduling. A mixed integer programming model of this problem is transformed into a CTCNN computation architecture by introducing a permutation matrix expression. A new computational energy function is proposed to express the objective besides all the constraints. In particular, the tradeoff problem existing among the penalty terms in the energy function is overcome by using time-varying penalty parameters. Finally, results tested on 3 different scale problems with 100 random initial conditions show that the network converges and can solve these problems in the reasonable time.