9713878 Spearman This award provides funding for the development of an approach to finite capacity scheduling that uses simulation-based optimization techniques and real-time information now available in modern manufacturing execution systems (MES). Our approach will be to incorporate powerful simulation optimization techniques with a fast manufacturing simulator to develop a scheduling methodology that incorporates variability, computes "optimal"' lead times, and provides sensitivity analysis. The specific issues we will address in our research include: (1) converting a "bill of process"' (i.e., the combination of a bill of material, the involved routings, and the processes) along with a set of demands into a fast "sample path" simulation model; (2) developing useful objective functions that use information found in an MES and that reflect the desires of production planners; (3) examine various gradient estimation and fast optimization methods; (4) develop methods for sensitivity analysis that provide information useful to a production planner to assist in making an infeasible production schedule feasible (e.g., adding capacity, pushing out due dates); (5) test the methods using the Virtual Factory Laboratory at Georgia Tech and in the plants of our industrial partners. If this research is successful it will improve manufacturing scheduling in a number of ways. First, it should improve existing MRP II systems by computing planned lead times that consider current and predicted congestion levels. As such, large existing software systems need only be retrofitted rather than replaced. Second, the system will explicitly consider variability and randomness. By considering these factors, appropriate lead time buffers can be set without extra "padding" thereby remaining competitive in terms of both quick response and the ability to meet commitments. Third, the developed methodology should be able to provide useful diagnostics for a schedule indicating which requirements are difficult to attain and how they might be relaxed. This should provide the ability to know how to make an infeasible schedule feasible in the most cost effective way.
