基于耦合瞬态混沌神经网络的同等并行机调度

在各种生产制造系统中都广泛存在着同等并行机调度. 本文提出了一种新的耦合瞬态混沌神经网络来求解同等并行机调度问题. 通过引入新的换位矩阵将该问题的混合整数规划模型转化为耦合瞬态神经网络的计算结构. 同时, 提出了新的计算能量函数, 使其能够包含所有约束和目标. 此外, 采用时变惩罚参数, 克服了能量函数中各惩罚项之间的权衡问题. 最后, 将该算法应用于求解 3 种不同规模的随机问题并进行仿真, 每种规模随机测试 100 次. 结果显示, 该算法能在合理的时间内收敛, 并求解出这些随机问题.

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

Scheduling jobs on identical machines is a situa- tion frequently encountered in various manufacturing systems. In this paper,a new coupled transiently chaotic neural net- work(CTCNN)is put forward to solve identical parallel ma- chine scheduling.A mixed integer programming model of this problem is transformed into a CTCNN computation architecture by introducing a permutation matrix expression.A new com- putational 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 over- come by using time-varying penalty parameters.Finally,results tested on 3 different scale problems with 100 random initial con- ditions show that the network converges and can solve these problems in the reasonable time.