Applying Feature Selection to Rule Evolution for Dynamic Flexible Job Shop Scheduling

Abstract

Dynamic flexible job shop scheduling is an optimization problem concerned with job assignment in dynamic production environments where future job arrivals are unknown. Job scheduling systems employ a pair of rules: a routing rule which assigns a machine to process an operation and a sequencing rule which determines the order of operation processing. Since hand-crafted rules can be time and effort-consuming, many papers employ genetic programming to generate optimum rule trees from a set of terminals and operators. Since the terminal set can be large, the search space can be huge and inefficient to explore. Feature selection techniques can reduce the terminal set size without discarding important information and they have shown to be effective for improving rule generation for dynamic job shop scheduling. In this paper, we extend a niching-based feature selection technique to fit the requirements of dynamic flexible job shop scheduling. The results show that our method can generate rules that achieves significantly better performance compared to ones generated from the full feature set.