Decision Tree Induction and Neural Network Solutions to Resource Constrained Scheduling Problems

Most scheduling solutions fail when uncertainty or unscheduled situations arise that were not accounted for. Also, neural network solutions have one weakness: these networks may solve the problems well but give no clear indications of how or why a particular problem is solved. One must try to decipher the network weights to understand the results. This ability to obtain knowledge from the weights is very much a research issue. In this project we:
  1. Updated the current simulation model to incorporate additional variables not accounted for in the earlier models. These included:
    1. supervisor preference;
    2. storage/holding tanks for intermediate material handling;
    3. shortages of labor and/or equipment, including maintenance downtime.
  2. Used decision tree induction and our current data to automatically create a rule base to solve this same scheduling problem.
  3. Examined possible hybrid neural network / decision tree solutions to this problem. The methods when combined resulted in an improved scheduling system.