Aiming at the problem of the low collaborative efficiency of outsourced processing of wallboard parts of a coating machine under a network collaborative manufacturing mode, this paper proposes a wallboard manufacturing resource composition optimization method based on the Improved Polar Bear Optimization (IPBO) algorithm. The processing process of the wallboard is analyzed, and the process-level splitting of the wallboard manufacturing task is completed; the required manufacturing resource service portfolio is determined, and the resource evaluation indicator system for key performance indicators of wallboard manufacturing resources is established; non-cooperative game decision-making is used to construct a wallboard manufacturing resource composition optimization model from two aspects, namely, quality indicators and flexibility indicators; an adaptive vision and mutation strategy is introduced to carry out the Polar Bear Optimization (PBO) algorithm. Finally, the improved algorithm is used to solve the wallboard manufacturing resource composition optimization model. The experimental results show that the IPBO algorithm reduces the average convergence time by 6.51% and the optimal convergence time by 9.26% compared with the suboptimal Dung Beetle Optimization (DBO) algorithm, and 65%–72% of the test points of the IPBO algorithm are more in line with the preference criteria of the Pareto frontier. Meanwhile, it demonstrates both validity and superiority in solving the problem of expanding the size of wallboards for coating machines.

Aiming at the problem of the low collaborative efficiency of outsourced processing of coating machine oven parts under the network collaborative manufacturing mode, this paper proposes a composition optimization method for coating machine oven-manufacturing services based on an improved sparrow search algorithm. We establish a framework for the service composition optimization problem on the oven manufacturing service platform; complete an evaluation of the manufacturing service quality of service indicators (QoS) and energy consumption indicators; construct a dual-objective service composition optimization mathematical model considering the QoS and energy consumption indicators; and embed the Tent chaotic mapping, elite reverse learning, and Lévy flight improvement differential evolution strategies into the sparrow search algorithm. We named this algorithm the LCSSA_DE algorithm, using it to solve the mathematical model of the manufacturing service combination problem of coating machine ovens, and obtain the optimal manufacturing service combination recommendation scheme. The experimental results demonstrate that this algorithm can effectively improve the convergence speed compared with the suboptimal multi-objective artificial vulture optimization algorithm (MOAVOA), with the average convergence time improved by 7.26%, avoiding falling into the local optimum during the search, while 69%–77% of the test points are more in line with the preference criteria of the Pareto frontier, and can be adapted to the optimization of the coating machine oven manufacturing service composition optimization problem at different scales.

Aiming at the problem of optimizing the composition of manufacturing resources in part-level outsourcing of sewing machine case manufacturing, this paper proposes a service composition optimization method based on bi-level programming. We analyze the structure and production process of sewing machine cases, determine the required service composition of manufacturing resources, and establish the evaluation indicator system of manufacturing service composition in line with the interests of multiple parties. We also introduce the idea of bi-level programming, construct the optimization model of manufacturing service composition of sewing machine cases based on bi-level planning, analyze the characteristics of NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm II) algorithm and the improvement strategy, and complete the solution of the optimization model of manufacturing service composition of sewing machine cases. The experimental results show that the composition optimization method and the algorithm improvement strategy can well solve the bi-level programming model of sewing machine cases in the case of complex feature information and avoid falling into the local optimal solution.

In response to the low efficiency of collaborative processing of sewing machine cases at the part level in network collaborative manufacturing, this paper proposes a sewing machine cases manufacturing service composition optimization method based on an improved multi-objective artificial hummingbird algorithm. The structure and production process of sewing machine cases are analyzed; a framework for service composition optimization in the sewing machine cases manufacturing service platform is established; the required manufacturing resource service composition is determined; and a dual-objective service composition optimization mathematical model that considers Quality of Service (QoS) indicators and flexibility indicators is constructed. Opposition-based learning strategies, roulette wheel selection strategies, and improved differential evolution strategies are embedded in the multi-objective artificial hummingbird algorithm, and the improved artificial hummingbird algorithm (ORAHA_DE) is used to solve the sewing machine cases manufacturing service composition optimization model. The experimental results show the effectiveness and superiority of this composition optimization method in solving the sewing machine cases manufacturing composition optimization problem while avoiding entrapment in a local optimum during the solution process, thereby achieving the composition optimization of sewing machine cases collaborative manufacturing services.

To address the collaborative optimization problem caused by the strong coupling between 2D sheet metal nesting and flexible shop floor scheduling in the sheet metal manufacturing process for color-separation ovens of satellite-type flexographic printing presses, this paper proposes a collaborative optimization method for nesting and scheduling based on a dual-flow graph attention network and proximal policy optimization. This problem involves inherent structural and resource symmetry in manufacturing operations and is further complicated by process precedence constraints, multi-resource competition, human–machine collaboration, assembly dependencies, and the dynamic coupling between nesting decisions and downstream production takt. Based on the composition of oven components and actual production methods, a collaborative optimization model integrating nesting and scheduling was constructed; a discrete-event simulation-driven joint scheduling environment was established to uniformly model the nesting, cutting, flexible machining, and assembly processes. On this basis, the collaborative nesting–scheduling process was formalized as a Markov decision process, clearly defining the state space, action space, reward function, and state transition mechanism. To enhance the state representation capabilities of the reinforcement learning agent in a high-dimensional discrete action space and under strongly constrained dynamic scheduling scenarios, this paper embeds a dual-stream graph attention network into the PPO framework to develop the DS-GAT-PPO algorithm. This algorithm simultaneously captures spatial nesting relationships among parts as well as temporal dynamic features such as equipment load, worker fatigue, and production takt time, thereby generating feasible and efficient joint scheduling plans. Experimental results show that the proposed method can reduce completion time by approximately 10.9–33.8% while maintaining a high sheet utilization rate; in a comparison of reinforcement learning algorithms, the proposed method achieved better completion times in most test cases and reduced the number of convergence steps by approximately 6.67–66.67%, validating its effectiveness in improving solution quality, convergence efficiency, and scheduling stability. These findings provide a foundation for further research and practical applications of collaborative nesting–scheduling optimization in dynamic manufacturing environments.

Aiming at the problem of difficult decision-making of manufacturing resource composition optimization in part-level outsourcing of sewing machine case manufacturing, this paper proposes a service composition optimization method based on bi-level programming. We analyze the structure and production process of sewing machine cases, determine the required service composition of manufacturing resources, and establish the evaluation index system of manufacturing service composition in line with the interests of multiple parties; introduce the idea of bi-level programming, and construct the optimization model of manufacturing service composition of sewing machine cases based on bi-level planning; we analyze the characteristics of NSGA-Ⅱ algorithm and the improvement strategy, and complete the solution of the optimization model of manufacturing service composition of sewing machine cases to achieve the combination optimization of the manufacturing services of the machine cases. The combination optimization model of sewing machine case manufacturing services is solved to realize the combination optimization of machine case manufacturing services. The experimental results show the effectiveness and superiority of the combination optimization method and algorithm improvement strategy in solving the combination optimization problem of sewing machine case manufacturing, and the combination optimization of sewing machine case cooperative manufacturing services is realized.