Dynamic programming for optimal control of setup scheduling with neural network modifications

Demonstrated is an optimal control solution to change of machine setup scheduling based on dynamic programming average cost per stage value iteration as set forth by M. Caramanis et al. (1991) for the 2-D case. The difficulty with the optimal approach lies in the explosive computational growth of the resulting solution. A method of reducing the computational complexity is developed using ideas from biology and neural networks. A real-time controller is described that uses a linear-log representation of state space with neural networks employed to fit cost surfaces.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>