The utilization of artificial intelligence (AI) techniques for the scheduling and sequencing of manufacturing systems has been investigated. These studies have included respective comparisons with "traditional" techniques. In addition, these studies have resulted in the development of an effective scheduling and sequencing methodology that considers the interrelation among the different elements of the manufacturing system. This methodology integrates neural networks, genetic algorithms, and operations research (OR) techniques. This methodology has evoked interest from industry and has the potential to support the development and implementation of agile manufacturing systems. This paper describes the methodology and the initial efforts toward the development of a two-level scheduling prototype.