Large-scale assembly production suffers from inefficient resource allocation, difficulties in coordinating interests across workstations, and severe congestion issues due to massive scale, complex tasks, and fluctuating constraints. To address these challenges, this study proposes a resource-efficient scheduling decision-making method based on a mixture pulsation model (DMMPM). The main contributions are as follows: 1) quantitative criteria for pulsation rhythm (takt-time) consistency in assembly production are defined, and a mixture equilibrium model for multiworkstation collaborative scheduling is developed, to integrate production rhythm alignment with workforce optimization within a coalition-driven profit maximization framework; 2) a spatiotemporally constrained task-allocation method driven by theoretical allocation batches is designed, balancing interstation resource demand conflicts and production rhythm synchronization requirements; and 3) a bi-level "scheduling-collaboration" architecture is proposed, where scheduling agents generate workstation-level strategies and collaboration agents coordinate cross-workstation coalition strategies through profit distribution, thereby enabling the efficient integration of decentralized decision-making and global optimization. The mathematical model is validated using ILOG CPLEX. Compared with conventional approaches, DMMPM significantly reduces the integrated scheduling cost and demonstrates superior decision-making capability and improved control of pulsation rhythm in large-scale aircraft manufacturing scenarios.

This study explores workforce resource optimization in aircraft assembly under a fixed production cycle time (CT), which is a critical challenge for enterprises operating pulsed assembly lines. Traditional methods often struggle due to limited operational data, weak generalization, and the lack of closed-loop adjustment mechanisms, which restrict their effectiveness in practical settings. To address these challenges, we propose a generation–prediction–scheduling (G-P-S) framework that establishes a closed-loop architecture for adaptive optimization. A diffusion-based generative model first augments sparse datasets with high-quality synthetic assembly scenarios. Then, a Transformer-based predictor trained on both real and generated data provides accurate multitype workforce demand forecasting. Finally, a CT-guided adaptive feedback mechanism integrates greedy scheduling with dynamic workforce adjustment, ensuring feasibility and efficiency under varying production conditions. Comparative studies demonstrate that the proposed framework achieves more accurate forecasting and more robust optimization performance than evolutionary and reinforcement learning baselines. Overall, the G-P-S framework offers a scalable and practical solution for intelligent resource optimization in complex assembly environments.

Aircraft assembly scheduling (AAS) grows increasingly complex in the presence of multiple uncertainties, given that these factors significantly influence assembly process efficiency and the optimal allocation of resources. Existing scheduling methods have exhibited their limitations in taking into consideration of the uncertainties related to assembly resources, the actual number of workers that may change in process, and the previous assembly experience with high productivity. To overcome these limitations, a workstation adjustment mechanism (WAM) is proposed to improve the availabilities of workstations. WAM is fully integrated with actual worker configurations to mitigate the shortages of workers in the task conversions in a schedule. A knowledge transfer-based multiobjective evolutionary algorithm (KT-MOEA) is developed as a systematic optimization framework for addressing multiobjective AAS problems. Moreover, a design of experiments systematically evaluates the impact of controllable variables across multiple instances, which enhances the robustness, interpretability, and generalizability of the proposed framework. This innovative approach systematically integrates transfer learning and the nondominated sorting genetic algorithm II to enhance optimization performance. Quantitative results demonstrate that the proposed KT-MOEA significantly enhances optimization performance, achieving up to 50% improvement in convergence (inverted generational distance), substantial gains in solution diversity (hypervolume), and at least a 7% enhancement in the quality of Pareto-optimal solutions compared with benchmark algorithms.

Traditional approaches for deriving NOxemissions from satellite observations assume observed NO2column loadings are certain, and apply either a fixed and smooth wind/NO2gradient, or complex modeling systems which are inflexible to changes in emissions of NO, NO2, and other co-emitted substances and emissions variability at high spatial/temporal resolution. This analysis leverages a mass-conserving and flexible system to compute daily and grid-based NOxemissions explicitly considering uncertainties in the observed TROPOMI NO2columns, wind divergence, and first-order chemical, physical, and thermodynamic processes. Considering a conservative 10% TROPOMI uncertainty, 84% of grids are revealed to have robust emissions and 48% of grids have 12-months of data per year, while community-standard approaches fail to retrieve more than 6-months of data per year on any grid in Northern China. The major factors behind these differences are the improved computation of the effects of natural wind variation due to geophysical factors with the dual-gradient term, handling the strong non-linearity of the spatial gradient operator, as well as buffering from first order chemical, physical, and thermodynamic terms. These findings aim to offer a more precise, accurate, and robust assessment of the observational uncertainties involved. In accordance with the principles of MVR (Monitor, Verify, and Report) mandated by the US and China EPAs, this study proposes a new approach to enhance both monitoring and verification techniques. By accurately identifying the temporal and spatial occurrence of NOxemissions, more effective and trustworthy mitigation strategies are supported.

Aircraft assembly scheduling (AAS) with multiconstraints, multitype workers, and the orders of tasks has become a key research focus in advanced industrial manufacturing. An elite gene retention metaheuristic algorithm (EGHA) is proposed in this article as a transfer knowledge generator, and the elite gene transfer learning heuristic algorithm (TL-EGHA) is also utilized as an optimized framework to tackle these issues for the AAS problem. Fully considered characteristics of the AAS problem, double time windows are proposed to deal with the constraints of worker type and space restrictions, and this guarantees that the proposed algorithm can obtain the solution quickly. The transfer learning strategy imports transfer knowledge associated with features and previous experiences, which prompts the initialization results closer to the task goals and supports dynamic adjustments of the parameters in TL-EGHA to enhance the global searching capability significantly. TL-EGHA has been a verified advancement in scheduling four real-world aircraft assembly lines by a comparative study with some existing scheduling algorithms, including well-known genetic transfer learning.

In this article, the genetic transfer learning (GTL) method is proposed for knowledge transfer of sustainable assembly manufacturing systems. Existing methods for the assembly line balancing problem, such as genetic algorithms (GAs), suffer from three significant limitations, i.e., tedious “trial-and-error” processes, no utilization of existing system solutions, and no consideration of new constraints on system reconfiguration. To address these problems, we propose GTL to migrate the knowledge of the GA setup in system reconfiguration. The contributions of this article are as follows. First, transfer pretreatment is performed to formulate knowledge of existing systems and adapt to the new constraints of future systems. Second, the similarity of existing and future systems is defined quantifiably to determine transfer conditions and avoid weak and negative transfer for maximizing knowledge transfer. Finally, the transfer strategy is made to determine the method and knowledge to be transferred. The case study of a computer assembly line shows that adopting transfer learning has helped to improve assembly line efficiency and sustainability.