Development of Inference Method for Efficient Constraint Processing and It's Application to Scheduling Problems

Abstract

An inference method for efficient processing of ordering constraints has been developed through combined use of look-ahead and constraint filtering in the generate-and-test like framework for constraint satisfaction, and applied to a knowledge system tool for job shop scheduling. The inference method is useful for providing a practical, tentative solution in scheduling problems where no feasible solution is available or where the existence of a feasible solution is unknown. A knowledge-based scheduling system with the proposed inference method has been generated and applied to job shop scheduling problems with 10, 100 and 200 jobs. From the results, it was concluded that the inferelnce method gives a tentative "quasi-feasible" solution and the optimum solution of job shop scheduling problems and that scheduling systems can be automatically generated owing to the precise formulation with assignment functions. It was confirmed that the constraint filtering contributes to the suppression of computer efforts of constraint satisfaction and that the look-ahead steps are optimized from computation trade-off between look-ahead and backtracking.