To address the issue that hybrid flow shop production struggles to handle order disturbance events, a dynamic scheduling model was constructed. The model takes minimizing the maximum makespan, delivery time deviation, and scheme deviation degree as the optimization objectives. An adaptive dynamic scheduling strategy based on the degree of order disturbance is proposed. An improved multi-objective Grey Wolf (IMOGWO) optimization algorithm is designed by combining the “job-machine” two-layer encoding strategy, the timing-driven two-stage decoding strategy, the opposition-based learning initialization population strategy, the POX crossover strategy, the dual-operation dynamic mutation strategy, and the variable neighborhood search strategy for problem solving. A variety of test cases with different scales were designed, and ablation experiments were conducted to verify the effectiveness of the improved strategies. The results show that each improved strategy can effectively enhance the performance of the IMOGWO. Additionally, performance analysis was conducted by comparing the proposed algorithm with three mature and classical algorithms. The results demonstrate that the proposed algorithm exhibits superior performance in solving the hybrid flow-shop scheduling problem (HFSP). Case validations were conducted for different types of order disturbance scenarios. The results demonstrate that the proposed adaptive dynamic scheduling strategy and the IMOGWO algorithm can effectively address order disturbance events. They enable rapid response to order disturbance while ensuring the stability of the production system.

To ensure an effective disturbance response and maintain continuous production in hybrid flow shops, this paper focuses on the design of a rescheduling method. A rescheduling model is constructed that minimizes the makespan, total tardiness, and scheme deviation degree. A hybrid rescheduling driving mechanism based on the latest completion time is designed to effectively trigger rescheduling. The Whale Optimization Algorithm (WOA) is improved by integrating the good point set theory, nonlinear control parameter strategy, and Differential Evolution (DE) algorithm. Moreover, non-dominated sorting and a dynamic external archive mechanism based on crowding distance are introduced to make it suitable for multi-objective optimization problems. The superiority of the Improved Multi-objective Whale Optimization Algorithm (IMOWOA) and the effectiveness of the improved mechanisms are verified through comparative experiments and ablation experiments. Taking the final assembly production line of an agricultural machinery equipment enterprise as an example, a rescheduling scheme is generated based on the practical production requirements, which verifies the feasibility and effectiveness of the proposed method.