Scheduling heuristic learning via genetic programming for dynamic flexible job shop scheduling with heterogeneous batch arrivals

Dynamic flexible job shop scheduling (DFJSS) is a challenging combinatorial optimisation problem that requires effective decision-making under dynamic environments. Although genetic programming (GP) has shown success in automatically learning scheduling heuristics, existing research predominantly addresses dynamic events involving single job arrivals, which does not always reflect real-world situations. In practice, heterogeneous batch arrivals where different jobs arrive simultaneously, are quite common and introduce a new decision-making challenge: handling multiple routing decisions (machine assignments) concurrently. However, this problem has received little attention in the literature. To fill this gap, we first formulate the DFJSS problem with heterogeneous batch arrivals. Furthermore, to effectively coordinate simultaneous routing decisions introduced by batch arrivals, GP is used to evolve scheduling heuristics to prioritise hybrid operation-machine pairs instead of jobs. An update strategy is incorporated to reflect the latest system status after each routing assignment, improving decision quality under dynamic changes. Experimental results across 18 scenarios demonstrate that using routing rules to prioritise pairs achieves the best average rank and outperforms compared methods. The effectiveness of the update strategy is also verified. In general, a single routing rule can be effectively applied to both concurrent and individual routing decisions.