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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.
Scheduling jobs on identical machines is a situa- tion encountered in various manufacturing systems. In this paper, a new coupled transiently chaotic neural net-work (CTCNN) is put forward to solve identical 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.