Energy-Efficient Scheduling for Distributed Hybrid Flowshop of Offshore Wind Blade Manufacturing Considering Limited Buffers

Amidst the backdrop of energy transition, scheduling problems in offshore manufacturing have emerged as critical challenges in marine engineering. However, the inherently coupled constraints of sequence-dependent setup times (SDST) and limited buffers (LB) have been largely overlooked. Therefore, this paper establishes the first multi-objective scheduling model, DHFSP-SDST&LB, specifically tailored for large components like turbine blades. A hybrid optimization algorithm, DDQN-MOCE, integrating an evolutionary algorithm (EA) and a double deep Q-network (DDQN), is proposed to overcome the inherent limitations of traditional MOEAs. In the EA component, a three-phase crossover and mutation policy is employed to generate offspring. In the DDQN component, the dimension-reduced feature vectors serve as the state input, and three makespan-oriented and two energy-oriented heuristic search actions are defined based on the knowledge. Finally, the optimal parameter combination is determined via Taguchi experimental design, and the effectiveness of DDQN-MOCE is evaluated on 36 instances and 1 industrial case. Experimental results demonstrate that DDQN-MOCE’s HV surpasses the second-best result by over 50% in 34 instances. It achieves the best GD, near-absolute dominance, and saves over 22% in total energy, with its high volume of solutions compensating for a minor weakness in spacing.