Declarative Decomposition and Dispatching for Large-Scale Job-Shop Scheduling

Job-shop scheduling problems constitute a big challenge in nowadays industrial manufacturing environments. Because of the size of realistic problem instances, applied methods can only afford low computational costs. Furthermore, because of highly dynamic production regimes, adaptability is an absolute must. In state-of-the-art production factories the large-scale problem instances are split into subinstances, and greedy dispatching rules are applied to decide which job operation is to be loaded next on a machine. In this paper we propose a novel scheduling approach inspired by those hand-crafted scheduling routines. Our approach builds on problem decomposition for keeping computational costs low, dispatching rules for effectiveness and declarative programming for high adaptability and maintainability. We present first results proving the concept of our novel scheduling approach based on a new large-scale job-shop benchmark with proven optimal solutions.