Open Access

ARTICLE

Research on Dynamic Scheduling Method for Hybrid Flow Shop Order Disturbance Based on IMOGWO Algorithm

Feng Lv^*, Huili Chu, Cheng Yang, Jiajie Zhang

School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China

* Corresponding Author: Feng Lv. Email:

(This article belongs to the Special Issue: Advances in Nature-Inspired and Metaheuristic Optimization Algorithms: Theory, Applications, and Emerging Trends)

Computers, Materials & Continua 2026, 86(3), 49 https://doi.org/10.32604/cmc.2025.072915

Received 06 September 2025; Accepted 28 October 2025; Issue published 12 January 2026

Abstract

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.

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Keywords

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