Investigation of Decision Making with Scheduling Rules Learned via Genetic Programming for Dynamic Flexible Job Shop Scheduling

Genetic programming (GP) has been popularly used to learn scheduling rules for dynamic flexible job shop scheduling. These scheduling rules serve as priority functions to prioritise candidate machines or operations at decision points. In the implementation level, a high prioritised machine and operation can be the ones with the highest or lowest priority value calculated with a scheduling rule in the decision making process. In theory, GP can adaptively evolve scheduling rules to accommodate different priority settings. However, research exploring the possible hidden differences during the evolutionary process remains limited. To fill this gap, this paper presents a comprehensive investigation into scheduling rules learned by GP under varying priority settings. The results show that while GP achieves similar performance in most investigated scenarios with different priority settings, GP-low where candidates with the lowest priority values are selected, can learn effective scheduling rules faster. Specifically, GP-low can learn smaller sequencing rules. Furthermore, visualisations of the scheduling rules illustrate how GP adaptively adjusts node positions to evolve effective rules. The study also highlights an inherent bias in initialised scheduling rules, which tend to prefer candidates with lower priority values in the examined scenarios. Moreover, GP-low exhibits a broader distribution of priority values. These findings can provide deeper insights into GP’s adaptive learning mechanisms and offer valuable guidance for decision-making of using scheduling rules.