Linear programming and associative memory matrices

It is pointed out that a significant aspect of the transportation problem is to study the sensitivity of the optimal shipping schedules to variations in the configuration of supplies and demands. The authors attempt to show experimentally that recent developments in the field of artificial neural networks hold promise for this area. The application discussed envisages treating a vector of supplies and demands as a stimulus vector, with the corresponding response vector being the optimal shipping schedule and objective function value. For a suitable set of training cases, the matrices R and S are produced, with each training case involving the solution of a transportation problem, for which SPEAKEASY (a standard software package) contains a conventional algorithm. The research objective is to use training information to define an associative memory matrix that will estimate response (solution) vectors associated with new stimulus (formulation) vectors