This paper presents an application of a two level mixed optimization method
on a machine scheduling problem of a government owned machine shop. Where
evolutionary algorithm methods are suitable for solving complex, discrete
space, and non-linear, discontinuous optimization problems; classical
direct-search optimization methods are suitable and efficient in handling
simple unimodal problems requiring less computation. Both methods are used at
two levels, the first level decides which machines to be used for the
machining operations and how much overtime (at extra cost) to be allotted to
each work order, the second level decides for which operation and on which
day the overtime should be allotted so as to attain its maximum benefit. A
sample problem has been solved by using the above methods and a range of
non-dominated solutions have been presented in a tabular form to enable the
production manager to choose his options based on the given criticality of
the work order.
A Mixed Optimization Method Based on Adaptive Kalman Filter and Wavelet Neural Network for INS/GPS During GPS Outages
