Xixing Li

With the growing emphasis on holistic management throughout the entire product lifecycle, multi-stage production models that integrate distributed manufacturing, transportation, and assembly processes have gradually attracted research attention. However, studies in this area remain relatively scarce. This paper addresses the fuzzy distributed assembly hybrid flow shop scheduling problem (FDAHFSP), comprehensively considering the entire production flow from manufacturing and transportation to final assembly. A mathematical model is first established with the objectives of minimizing the fuzzy total weighted earliness/tardiness and the fuzzy total energy consumption. To effectively solve this problem, a Q-learning-based adaptive NSGA-II (Q-ANSGA) is proposed. The algorithm incorporates a hybrid strategy combining multiple rules to enhance the quality of the initial population. Additionally, a Q-learning-based adaptive parameter adjustment mechanism is designed to dynamically optimize genetic algorithm parameters, thereby improving the algorithm’s search efficiency and convergence performance. Furthermore, eight neighborhood search operators are developed, and an iterative greedy strategy is integrated to guide the local search process. Finally, comprehensive experiments on 45 test instances are conducted to evaluate the effectiveness of each improvement component and the overall performance of Q-ANSGA. Experimental results demonstrate that the proposed algorithm achieves superior performance in solving the FDAHFSP due to its systematic enhancements.

In lace textile manufacturing, the dyeing process in parallel machine environments faces challenges from sequence-dependent setup times due to color family transitions, machine eligibility constraints based on weight capacities, and probabilistic re-dyeing operations arising from quality inspection failures, which often lead to increased tardiness. To tackle this multi-constrained problem, a stochastic integer programming model is formulated to minimize total estimated tardiness. A novel symmetry-driven two-population collaborative differential evolution (TCDE) algorithm is then proposed. It features two symmetrically complementary subpopulations that achieve a balance between global exploration and local exploitation. One subpopulation employs chaotic parameter adaptation through a logistic map for symmetrically enhanced exploration, while the other adjusts parameters based on population diversity and convergence speed to facilitate symmetry-aware exploitation. Moreover, it also incorporates a symmetrical collaborative mechanism that includes the periodic migration of top individuals between subpopulations, along with elite-set guidance, to enhance both population diversity and convergence efficiency. Extensive computational experiments were conducted on 21 small-scale (optimally validated via CVX) and 15 large-scale synthetic datasets, as well as 21 small-scale (similarly validated) and 20 large-scale industrial datasets. These experiments demonstrate that TCDE significantly outperforms state-of-the-art comparative methods. Ablation studies also further verify the critical role of its symmetry-based components, with computational results confirming its superiority in solving the considered problem.

The interactive grasp of robots can grasp the corresponding objects according to the user’s choice. Most interactive grasp methods based on deep learning comprise visual language and grasp detection models. However, in existing methods, the trainability and generalization ability of the visual language model is weak, and the robot cannot cope well with grasping small target objects. Therefore, this paper proposes a 6-DoF grasp detection method guided by visual language, which converts text instructions and RGBD images of the scene to be grasped into inputs and outputs for the 6-DoF grasp posture of the object corresponding to the text instructions. In order to improve the trainability and feature extraction ability of the visual language model, a multi-head attention mechanism combined with hybrid normalization is designed. At the same time, a local attention mechanism is introduced into the grasp detection model to enhance the global and local information interaction ability of point cloud data, thereby improving the grasping ability of the grasp detection model for small target objects. The method proposed in this paper first uses the improved visual language model to predict the plane position information of the target object, then uses the improved grasp detection model to predict all the graspable postures in the scene, and finally uses the plane position information to filter out the graspable postures of the target object. The visual language model and grasp detection model proposed in this paper have achieved excellent performance in various scenarios of public datasets while ensuring a specific generalization ability. In addition, we also conducted real grasp experiments, and the 6-DoF grasp detection method based on visual language guidance proposed in this paper achieved a grasp success rate of 95%.