This thesis addresses the development and implementation of real-time scheduling and control decision-making in hierarchical manufacturing environments. The objective was to develop a prototype of a "controller" for a single manufacturing machine. This prototype will serve as an important tool to study the integration of several functions and the utilization of status data to evaluate scheduling and control decision alternatives. The emphasis is on creating a prediction capability to aid in assessing the long-term system performance impact resulting from decisions made and environmental changes. This "look-ahead" capability is implemented by using neural networks, simulation, and genetic algorithms (GAs). Neural networks predict the behavior of different sequencing policies (i.e., dispatching rules) available in the system. This prediction mechanism could reduce significantly the alternatives available. The contribution of the GAs to the decision- making process is the development of a "new" scheduling rule based on a "building blocks" procedure initiated by the neural networks. GAs have been selected due to the extreme difficulty of the direct application of traditional methodologies. In addition, this prototype could be part of a larger hierarchical system. The research findings and the prototype developed have direct applications in the construction of real-time systems that are capable of using adaptive status data and could gracefully degrade with unforeseen situations.