The empowerment of lean intelligent manufacturing technologies has provided a solid foundation for enterprises to achieve a balance between economic benefits and sustainable development. In production workshops, various disruptive factors, especially in multi-variety small-batch production environments, often lead to deviations from the planned schedule. This creates an urgent need to enhance the workshop’s dynamic responsiveness and self-regulation capabilities. Existing single-trigger mechanisms in job shops focus on changes in overall performance or deviations from production goals but lack a representation of the varying degrees of impact on different equipment under multiple disturbances. This results in either over-scheduling or under-scheduling in terms of scope, thereby impacting the optimization of production efficiency and resource utilization. To address this, this paper proposes a method for coordinated decision-making on rescheduling timing and location in intelligent job shops under disturbance environments. First, by analyzing the relationship between disturbance impact and the scope of rescheduling implementation, a mapping relationship is established between disturbance impact and disturbance response hierarchy. A trigger is set up on each piece of equipment to characterize the differences in the degree of impact on different equipment, which not only reduces the complexity of disturbance information processing but also provides support for specific location decisions for disturbance response. Second, a decision module for the triggers is constructed using a multilayer perceptron, establishing a mapping relationship between process and workpiece data attributes and response categories. Based on the basic processing units of the manufacturing process and the relevant quantitative indicators of the processed objects, disturbance response strategies are generated. Finally, through a case study, the proposed method is evaluated and validated in an intelligent factory setting. The new rescheduling decision support method can effectively make timing and location decisions for disturbance events.

Growing environmental concerns and regulatory pressures are prompting firms to re-examine packaging design to advance sustainability. Focusing on eco-designed retail packaging in the new-style milk tea industry, this study investigates how specific attributes of eco-designed retail packaging influence consumers’ purchase intention. Data were collected from 425 university students in Wuhan. We measured eco-designed retail packaging (ECRP) with a six-dimension scale (functional, aesthetic, eco-materials, eco-information, eco-production, and innovation) and tested the mediating role of green perceived value (GPV) using structural equation modeling (SEM). Results show differentiated effects of ECRP dimensions on GPV and purchase intention. Functional design and clear eco-information increase both GPV and purchase intention, whereas using eco-materials while directly raising purchase intention reduces GPV. Aesthetics and innovation mainly operate through direct enhancement of purchase intention rather than via GPV. GPV mediates part of the effects of functional attributes, eco-materials, and eco-information on purchase intention. The findings imply that optimizing functionality, information clarity, and material choices in eco-designed retail packaging can simultaneously elevate GPV and purchase intention. As green packaging becomes an industry imperative, this study provides theoretical and practical guidance for sustainable packaging innovation and green industry development.

Production scheduling and machine tool intermittent state control separately influence a workshop’s machining and intermittent energy consumption. Effective scheduling decisions and intermittent state control are crucial for optimizing the overall energy consumption in the workshop. However, the scheduling scheme determines the machine tool intermittent durations, which imposes strong constraints on the decision-making process for intermittent state control. This makes it difficult for intermittent state control to be used in providing feedback and optimizing scheduling decisions, significantly limiting the overall energy-saving potential of the workshop. To this end, a workshop energy-saving scheduling method is proposed integrating machine tool intermittent state control. Firstly, the variation characteristics of workshop machining energy consumption, machine tool intermittent durations, and intermittent energy consumption are analyzed, and an energy-saving optimization strategy is designed. Secondly, by incorporating variables such as intermittent durations, intermittent energy consumption, and variable operation start time, a multi-objective integrated optimization model is established. Thirdly, the energy-saving optimization strategy is integrated into chromosome encoding, and multiple crossover and mutation genetic operator strategies, along with a low-level selection strategy, are introduced to improve the NSGA-II algorithm. Finally, the effectiveness of the proposed method is verified through a machining case. Results show that the generated Gantt chart reflects both production scheduling and intermittent state control decision outcomes, resulting in a 1.51% reduction in makespan, and 3.90% reduction in total energy consumption.

Accurately predicting recycling prices at battery recycling sites helps reduce transportation and dismantling costs, ensures economies of scale in the recycling, and supports the sustainable development of the new energy vehicle industry. However, this prediction typically relies on easily accessible surface data, such as battery characteristics and market prices. These data have complex correlations with recycling price, general price prediction methods have low prediction accuracy. To this end, an improved prediction method is proposed to enhance the accuracy of predicting recycling prices through surface data. Firstly, factors influencing recycling prices are selected based on self-factor and market fluctuations, a bidirectional denoising autoencoder and support vector regression model (BDAE-SVR) is established. BDAE is used to adjust the weights of influencing factors to remove noise, extract features related to recycling price. The extracted features are introduced into the SVR model to establish a correspondence between the features and recycling price. Secondly, to have better applications for different batteries, the Grey Wolf algorithm (GWO) is used to adjust the SVR parameters to improve the generalization ability of the prediction model. Finally, taking retired power batteries as an example, the effectiveness of the method is verified. Compared with methods such as random forest (RF), the RMSE predicted by BDAE is decreased from 1.058 to 0.371, indicating better prediction accuracy.

Remanufacturing is a core pathway for value-added utilization of waste resources and promoting sustainable manufacturing. Its process reliability directly determines product service safety and recycling benefits. Given that remanufacturing targets high-value personalized end-of-life components, stringent reliability requirements are imposed to avoid economic losses from “secondary scrapping”. However, the small-batch production mode results in limited data, and complex causal interactions among failure morphologies, process parameters, and reliability render traditional models prone to capturing spurious correlations, leading to prediction deviations. Therefore, a method is proposed that extracts causal knowledge from historically similar process samples to guide reliability prediction. First, a component process similarity assessment and data fusion method based on process symbol entropy is proposed. The assessment of dissimilarity between process routes is conducted by quantifying global structural consistency through the Longest Common Subsequence (LCS) and local distributional variance through positional entropy. Furthermore, the reliability data of similar processes are fused to expand the sample set. Second, a knowledge modeling method for mapping the influencing factors of process reliability based on causal gradient derivation is proposed. The PC algorithm, combined with a bootstrap strategy, is applied to extract the causal structural relationships among influencing factors from the training portion of the augmented dataset. Subsequently, causal effects are estimated and converted into causal gradients, which quantitatively reveal the network transmission pathways and influence intensities of various factors on process reliability. Finally, a process reliability prediction model based on causality knowledge supervision is established. The prior causal gradient is embedded as a regularization term in the training of the Multilayer Perceptron network. By adjusting the direction of network parameter updates, the model’s predictive accuracy is enhanced. Taking the machine tool spindle as the research object, the proposed method is validated under small-sample conditions. The results demonstrate significant improvements in both R² and RMSE compared with standard models. The similarity-based data fusion raises the R² of the conventional MLP from 0.7145 to 0.9012, and the causal gradient regularization further increases it to 0.9320 with the RMSE reduced from 0.3483 to 0.2548.