Abstract
In this study we propose a heuristic scheduling approach to the minimization of maximum completion time of the general job-shop scheduling problems. The fundamental idea of this approach is based on the ability of human scheduler's pattern recognition, and we have tried to construct the similar manner in our heuristic procedure. We firstly divide the jobs into job groups based on the similarity of their flow patterns, and then generate sub-schedules for each job group separately. Finally we integrate the sub-schedules into the final global schedule with the aid of eliminating procedure of the operation conflicts. In order to test the effectiveness of the proposed approach, we have conducted the computational experiment varying the number of jobs from six to twenty, and the number of machines from five to fifteen. By comparison experiments with neural network approach and simulated annealing, the proposed approach has produced good solutions in reasonable computing times.
