Drawing inspiration from manufacturing production processes like chemical and steel manufacturing, the distributed blocking flowshop scheduling problem with preventive maintenance and sequence-dependent setup times (DBFSP/PM/SDST) is studied. First, it is described by a mixed-integer linear programming model with the objective of minimizing the total flowtime. Second, we propose a Q-learning and learning mechanism co-driven approach, integrating it into the discrete grey wolf optimization algorithm (DGWO_Q). In the algorithm, the neighborhood search structure is adjusted using Q-learning based on dynamic feedback from the environment. The balance between exploration and exploitation can be improved by introducing learning mechanisms in the search phase that can guide the grey wolf as it approaches the prey. Furthermore, a differential hunting strategy is designed to prevent the algorithm from falling into local optima. Third, a heuristic that enhances the quality of the initial solution is proposed for the problem characteristics. Finally, the proposed DGWO_Q is compared with four conventional efficient algorithms in numerical experiments on 225 instances of different sizes. Experimental results show that the DGWO_Q algorithm demonstrates excellent performance across test cases of various scales, effectively reducing production cycle time, setup times and the impact of maintenance downtime on production efficiency. It provides an efficient intelligent optimization approach for solving the complex scheduling problem.

The hybrid flow shop scheduling problem (HFSP) is an extension of the classic flow shop scheduling problem and widely exists in real industrial production systems. In real production, sequence-dependent setup times (SDST) are very important and cannot be neglected. Therefore, this study focuses HFSP with SDST (HFSP-SDST) to minimize the makespan. To solve this problem, a mixed-integer linear programming (MILP) model to obtain the optimal solutions for small-scale instances is proposed. Given the NP-hard characteristics of HFSP-SDST, an improved artificial bee colony (IABC) algorithm is developed to efficiently solve large-sized instances. In IABC, permutation encoding is used and a hybrid representation that combines forward decoding and backward decoding methods is designed. To search for the solution space that is not included in the encoding and decoding, a problem-specific local search strategy is developed to enlarge the solution space. Experiments are conducted to evaluate the effectiveness of the MILP model and IABC. The results indicate that the proposed MILP model can find the optimal solutions for small-scale instances. The proposed IABC performs much better than the existing algorithms and improves 61 current best solutions of benchmark instances.