- author = "Xiaolong Chen and Junqing Li and Zunxun Wang and Qingda Chen and Kaizhou Gao and Quanke Pan",
- title = "Optimizing Dynamic Flexible Job Shop Scheduling Using an Evolutionary Multi-Task Optimization Framework and Genetic Programming",
- journal = "IEEE Transactions on Evolutionary Computation",
- keywords = "genetic algorithms, genetic programming, Dynamic scheduling, Job shop scheduling, Heuristic algorithms, Optimisation, Real-time systems, Multitasking, Production, Evolutionary computation, Vehicle dynamics, Cranes, dynamic flexible job shop scheduling problem, evolutionary multi-task optimisation, hyperheuristics",
- ISSN = "1941-0026",
-
DOI = "
doi:10.1109/TEVC.2025.3543770",
- abstract = "Driven by the evolution of smart and sustainable manufacturing paradigms under Industry 5.0, which emphasize adaptability, connectivity, and data-driven decision-making, the dynamic flexible job shop scheduling problem (DFJSSP) has emerged as a critical area of research. The DFJSSP involves scheduling jobs in a highly dynamic and uncertain manufacturing environment where new tasks are continually introduced, further complicating the scheduling process. In this study, the DFJSSP is extended to incorporate single crane transportation and sequence-dependent setup times, reflecting real-world manufacturing constraints. To tackle this multifaceted problem, we introduce a novel approach, i.e., a multipopulation-based evolutionary multi-task optimisation(EMTO) framework. Additionally, the genetic programming algorithm is employed as a generative hyperheuristic to deal with the dynamic uncertainties in the shop floor. Two components are collaborated to optimise two objectives, i.e., minimizing the maximum completion time and the total tardiness. Furthermore, a dynamic transfer ratio is proposed, allowing the proportion of knowledge transfer to adapt throughout the iteration process, balancing convergence speed with population diversity. The results demonstrate that both the EMTO framework and the dynamic transfer ratio significantly enhance the performance of the algorithm. Compared to well-known constructive heuristics and reinforcement learning algorithm, the proposed approach enables parallel resolution of multiple optimisation objectives, leading to enhanced scheduling efficiency and adaptability in dynamic manufacturing environments.",
-
notes = "Also known as \cite{10896746}
Department of Mathematics, Yunnan Normal University, Kunming, Yunnan, China School of Information Science and Engineering, Shandong Normal University, Jinan, China",
