Sequencing with earliness and tardiness penalties: a review
Operations Research
Weighted-tardiness scheduling on parallel machines with proportional weights
Operations Research
Early/tardy scheduling with sequence dependent setups on uniform parallel machines
Computers and Operations Research
Parallel machine scheduling with earliness and tardiness penalties
Computers and Operations Research
Computers and Industrial Engineering
Parallel machine earliness and tardiness scheduling with proportional weights
Computers and Operations Research
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
INFORMS Journal on Computing
A coupled gradient network approach for static and temporal mixed-integer optimization
IEEE Transactions on Neural Networks
A review on evolution of production scheduling with neural networks
Computers and Industrial Engineering
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This paper considers the earliness and tardiness problem of sequencing a set of independent jobs on non-identical multi-machines, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. A coupled gradient network approach is employed to provide a shop scheduling analysis framework. The methodology is based on a penalty function approach used to construct the appropriate energy function and a gradient type network. The mathematical formulation of the problem is firstly presented and six coupled gradient networks are constructed to model the mixed nature of the problem. After the network architecture and the energy function were specified, the dynamics are defined by steepest gradient descent algorithm.