Dynamic hybrid flow shop scheduling problems (DHFSP) are critical in modern manufacturing systems, where uncertainties such as order fluctuations and equipment failures pose significant challenges. Traditional exact methods and metaheuristics struggle to meet real-time decision-making requirements under such dynamic conditions. Heuristic dispatching rules (HDRs) have been widely adopted for their rapid response capabilities. In recent years, large language models (LLMs) have demonstrated remarkable capabilities in code generation and logical reasoning, showing promising potential for automated HDR design. However, existing LLM-based methods predominantly adopt single-population evolution strategies, which suffer from insufficient population diversity, limited semantic-level reasoning, and premature convergence, thereby frequently becoming trapped in local optima. To address these challenges, this paper proposes a personalized multi-island reflective evolution framework that assigns distinct exploration personalities to multiple parallel sub-populations and incorporates an LLM-driven semantic reflection mechanism to achieve efficient search space coverage and enhanced exploration depth. The framework employs a two-stage strategy: offline training constructs a robust rule library through diversified dynamic scenarios, while online application enables rapid real-time decision-making. Experimental results on 300 test instances demonstrate that the proposed method outperforms traditional HDRs, conventional evolutionary rule generation methods, and state-of-the-art LLM-based approaches, exhibiting superior stability and generalization capability.

Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities, resulting in improved rationality of process routes and high-quality and efficient production. Hence, the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However, when performing this integrated optimization, the uncertainty of processing time is a realistic key point that cannot be neglected. Thus, this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem, FIPPS considers the fuzzy process time in the uncertain production environment, which is more practical and realistic. However, it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem, this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover, the critical path searching method is introduced according to the triangular fuzzy number, allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms, the results obtained by MSCOA show significant advantages, thus proving its effectiveness in solving the FIPPS problem.