Evolving coordination schemes in real-time production scheduling

In the recent past, several algorithms have emerged for real-time production scheduling which are typically based upon one or more of the following techniques: integer programming, artificial intelligence/heuristic, and single trial simulations. A critical assumption in nearly every application of the above methodologies is that both processing outcomes and durations are deterministic. Real world manufacturing systems are characterized by three primary types of uncertainties. The first type of uncertainty arises from the decomposition of the overall manufacturing problem. Specifically, the real-time production scheduler must be integrated with other manufacturing functions, particularly production planning and process coordination/control. The latter functions possess detailed information that is beyond the scope of direct consideration by the real-time scheduler, yet the actions of the other functions do influence the production scheduling problem. The second source of uncertainty arises from our inability to precisely specify the constraints associated with the decision-making. The final source of uncertainty pertains to the stochastic nature of the manufacturing process. Davis and Jones have developed a real-time production scheduler which will permit existing scheduling technologies to interact with other functional entities within the manufacturing system. This paper will concentrate upon the evolving coordination schemes that will be implemented within the proposed algorithm. In particular, the consequences derived from the consideration of multiple-criteria decision-making within a manufacturing hierarchy will be explored. Methodologies to address all forms of uncertainty will be discussed. Finally, the experimental testing of the coordinating procedures using hierarchical simulation will be outlined.