Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling

Dynamic flexible job-shop scheduling (DFJSS) is a challenging combinational optimization problem that takes the dynamic environment into account. Genetic programming hyperheuristics (GPHH) have been widely used to evolve heuristics for scheduling. A proper selection of terminal set critical factor success GPHH. However, there wide range features can capture different characteristics state. Moreover, importance feature unclear from one scenario another. The irrelevant and redundant may lead performance limitations. Feature an important task select relevant complementary features. little work has considered in GPHH DFJSS. In this article, novel two-stage framework with designed only selected DFJSS automatically. Meanwhile, individual adaptation strategies are proposed utilize information both investigated individuals during process. results show algorithm successfully achieve more interpretable fewer unique smaller sizes. addition, reach comparable heuristic quality much shorter training time.