A GRASP FOR SCHEDULING PRINTED WIRING BOARD ASSEMBLY

The assembly of printed wiring boards (PWBs) typically involves the co ordination of thousands of components and hundreds of part numbers in a job shop environment with up to 50 different processes and workstati ons. In this paper we present a greedy randomized adaptive search proc edure or GRASP, for solving the daily scheduling problem that is found in such environments. The advantages of the proposed methodology are its ability to respond quickly to changing organizational goals, revis ed customer requests, and a multitude of shop-floor contingencies. A f lexible lot-sizing heuristic with user overrides allows the scheduling algorithm to alter the production strategy in the face of random dist urbances, such as machine failures, component stockouts, and demand pe rturbations. The algorithms, embedded in a decision support system, ha ve been implemented at Texas Instruments' (TI) Austin facility. This f acility assembles boards for internal use and for a growing number of external customers. Before implementation it was necessary to convince management that the new approach would significantly outperform the s cheduling methods that were currently in use. We were able to do this by running a series of experiments using real data that compared TI's rule set for scheduling starts and WIP with our methodology. The resul ts indicated that over 10% increase in net revenues could be achieved with the GRASP and that comparable improvements in cycle time, flowlin e balance, and throughput could also be realized. Operational experien ce over the last two years has borne this out.