A novel hybrid hyper-heuristic approach for no-wait sequence-dependent flowshop group scheduling problems

This study proposes a novel hybrid hyper-heuristic approach based on the memetic algorithm and artificial rabbits optimization (ARO) algorithm for the no-wait flowshop group scheduling problem with sequence-dependent setup times. The ARO algorithm is used to generate the initial population, which is then evolved implementing a memetic-algorithm-based hyper-heuristic framework incorporating multiple crossover, mutation and hill climbing operators. Each operator and its parameters are regarded as low-level heuristics and are dynamically selected based on scores derived from a reinforcement learning mechanism. The performance of the proposed algorithm is rigorously evaluated on 270 benchmark instances widely used in the literature. Comparative analyses against two state-of-the-art simulated annealing approaches demonstrate the clear superiority of the proposed method, which achieves better results in 73% and 57% of the instances. These results not only highlight the robustness and effectiveness of the developed approach but also establish a strong foundation for its potential in solving complex scheduling problems. Notably, this study represents the first successful application of the ARO algorithm in the domain of discrete optimization, and its integration with a hyper-heuristic framework further amplifies its performance, setting a new benchmark for future research.