Knowledge-based decision support system for scheduling in a flexible flow system

Decision Support Systems (DSSs) are necessary resources for most complex decision making situations. The environment under which the DSSs function are, for the most part, evolving and, hence the DSSs should have the capability to adapt themselves as per the changes in the characteristics of the environment. An adaptive DSS that can function in a dynamic environment is proposed in this thesis, incorporating simulation modeling and inductive learning, to improve the overall performance of the system.