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This paper proposes a comprehensive Mixed-Integer Linear Programming (MILP) formulation for the simultaneous scheduling of machines and Automated Guided Vehicles (AGVs) within a partitioned Flexible Manufacturing System (FMS). The main objective is to numerically optimize the simultaneous scheduling of machines and AGVs while considering various workshop layouts and operational constraints. Three different workshop layouts are analyzed, with varying numbers of machines in partitioned workshop areas A and B, to evaluate the performance and effectiveness of the proposed model. The model is tested in multiple scenarios that combine different layouts with varying numbers of workpieces, followed by an extension to consider dynamic initial conditions in a more generalized MILP framework. Results demonstrate that the proposed MILP formulation efficiently generates globally optimal solutions and consistently outperforms a greedy algorithm enhanced by A*-inspired heuristics. Although computationally intensive for large scenarios, the MILP’s optimal results serve as an exact benchmark for evaluating faster heuristic methods. In addition, the study provides practical insight into the integration of AGVs in modern manufacturing systems, paving the way for more flexible and efficient production planning. The findings of this research are expected to contribute to the development of advanced scheduling strategies in automated manufacturing systems.

Mobile robots play an important role in smart factories, though efficient task assignment and path planning for these robots still present challenges. In this paper, we propose an integrated task- and path-planning approach with precedence constrains in smart factories to solve the problem of reassigning tasks or replanning paths when they are handled separately. Compared to our previous work, we further improve the Regret-based Search Strategy (RSS) for updating the task insertions, which can increase the operational efficiency of machining centers and reduce the time consumption. Moreover, we conduct rigorous experiments in a simulated smart factory with different scales of robots and tasks. For small-scale problems, we conduct a comprehensive performance analysis of our proposed methods and NBS-ISPS, the state-of-the-art method in this field. For large-scale problems, we examine the feasibility of our proposed approach. The results show that our approach takes little computation time, and it can help reduce the idle time of machining centers and make full use of these manufacturing resources to improve the overall operational efficiency of smart factories.

Importance-performance analysis (IPA) is widely used for needs analysis, product positioning, and strategic planning in product design. Previous research on IPA often employs single-source data such as customer surveys or online reviews with unavoidable subjective bias. In contrast, product maintenance records provide objective information on product quality and failure patterns, which can be cross-validated with customers’ personal experiences from surveys or online reviews. In this paper, we propose an integrated framework for conducting IPA from online reviews and product maintenance records jointly. An attribute-keyword dictionary is first established using keyword extraction and clustering methods. Then, semantic groups, including product attributes and associated descriptions, are extracted using dependency parsing analysis. The sentiment scores of identified product attributes are determined by a voting mechanism using two pre-trained sentiment analysis models. The importance of product attributes in IPA is estimated from the impact of sentiments of each product attribute on product ratings with the extreme gradient boosting (XGBoost) model, while the performance is estimated from the sentiment scores of online reviews or the quality statistics from product maintenance records. In addition, we propose two methods to validate the IPA results, in which the IPA results are compared with the actual product improvements on the market or compared with the analysis of customer reviews from different time periods, respectively. The validated IPA results from online reviews and maintenance records are then integrated to obtain a more comprehensive understanding of customer needs. A case study of passenger vehicles is used to demonstrate the framework. The proposed framework enables automatic data processing and can support companies in making efficient design decisions with more comprehensive perspectives from multisource data.

By employing thin section analysis, scanning electron microscopy (SEM), homogenization temperatures of fluid inclusions, and carbon–oxygen isotope analysis of carbonate cements, this study conducted a temporal-quantitative investigation into the porosity evolution of relatively high-quality reservoirs in the Second Member of the Xujiahe Formation (Xu-2 Member) in the Xinchang area of western Sichuan. The analysis focused on quantifying porosity loss due to compaction, cementation, and porosity enhancement from dissolution. Results indicate that compaction exerted the most significant impact on reservoir quality in the Xu-2 Member, causing over 70% of total porosity loss. Cementation processes, including carbonate cements, silica cements, and authigenic chlorite, further degraded reservoir properties. Authigenic chlorite precipitated earliest at burial depths of 600–800 m, while authigenic quartz and carbonate cements persistently affected the reservoir at depths of 2000–5000 m, reducing porosity by at least 10% (up to 21%). Dissolution processes initiated at approximately 3500 m burial depth, generating secondary porosity of ≥2%, with a maximum increase of 16%. Integrating these findings with the natural gas accumulation history, the coupling relationship between pore evolution and gas accumulation was elucidated. The study reveals that reservoir tightness in the Xu-2 Member developed at burial depths of 4050–5300 m, with large-scale gas accumulation predominantly occurring prior to reservoir densification. The findings provide critical guidance for identifying high-quality tight sandstone reservoirs and optimizing exploration targets in the Xu-2 Member of the Xinchang area, Western Sichuan Basin, thereby supporting efficient development of regional tight gas resources.

Choice modeling is important in transportation planning, marketing and engineering design, as it can quantify the influence of product attributes and customer demographics on customers’ choice behaviors. Consumer studies suggest that customers’ choice-making process often consists of two different stages: customers first consider subsets of available products on the market, and then make the final choice from the subsets. As existing preference modeling is mostly focused on the choice stage, there is a need to develop methods for understanding customer preferences at both stages, and investigate how customer preferences change from “consideration” to “choice”, and whether such changes will be consistent over time. In this paper, we study customers’ consideration and purchase behaviors in China’s auto market using multi-year survey datasets. We demonstrate how descriptive network analysis and analytic network models (bipartite Exponential Random Graph Model (ERGM)) capture the change of customers’ preferences from the consideration stage to the choice stage in multiple consecutive years. Our results show that factors such as fuel consumption per unit power, car make origin, and place of production influence customers’ considerations and final purchase decisions in different ways, and this difference between consideration and purchase is consistent over time. The main contribution of this study is that we validate the two-stage network-based modeling approach and its utility in preference elicitation using multiple-year dataset, which sheds lights on understanding the trend of customers’ consideration and choice behaviors across years. Our study also contributes to a refined interpretation of the ERGM results with categorization of continuous variables into ranges, which shows that customer choice decisions may be more qualitatively influenced by product attributes rather than quantitatively. Our approach is generic and thus can be applied to solving broader choice modeling problems, such as the transportation mode selection and the adoption of clean technology (e.g., electric vehicles).

PurposeThe purpose of this paper is to present an integrated data-driven framework for processing and analyzing large-scale vehicle maintenance records to get more comprehensive understanding on vehicle quality.Design/methodology/approachWe propose a framework for vehicle quality analysis based on maintenance record mining and Bayesian Network. It includes the development of a comprehensive dictionary for efficient classification of maintenance items, and the establishment of a Bayesian Network model for vehicle quality evaluation. The vehicle design parameters, price and performance of functional systems are modeled as node variables in the Bayesian Network. Bayesian Network reasoning is then used to analyze the influence of these nodes on vehicle quality and their respective importance.FindingsA case study using the maintenance records of 74 sport utility vehicle (SUV) models is presented to demonstrate the validity of the proposed framework. Our results reveal that factors such as vehicle size, chassis issues and engine displacement, can affect the chance of vehicle failures and accidents. The influence of factors such as price and performance of engine and chassis show explicit regional differences.Originality/valuePrevious research usually focuses on limited maintenance records from a single vehicle producer, while our proposed framework enables efficient and systematic processing of larger-scale maintenance records for vehicle quality analysis, which can support auto companies, consumers and regulators to make better decisions in purchase choice-making, vehicle design and market regulation.

Smart community microgrids are capable of efficiently addressing the energy and environmental challenges faced by cities. However, the inherent instability of renewable energy sources and the diverse nature of user demands pose challenges to the safe operation of community power systems. In this article, we first introduce a comprehensive system architecture, and an operational framework based on Energy Internet of Things (EIoT), which considers system‐level safety, reliability, and cost‐effectiveness, thereby enhancing the system’s coordination and performance. Next, we propose a bi‐level coordinated optimization method based on the users’ electricity consumption behaviors. At the planning level, we employ a multiobjective optimization approach to determine the most suitable microgrid configurations that cater to the requirements of various user groups, and the results derived from adaptive weight particle swarm optimization (PSO) algorithm are fed back to the operational level. At the operational level, a 24‐h time scale is selected, and the economic efficiency problem is addressed using a linear programming method. The operational decision results are then fed back to the planning level for major maintenance of the microgrid system. Meanwhile, we employ trend prediction methods to categorize maintenance tasks into short‐term and long‐term operations based on an analysis of daily operational data. The short‐term prediction results can serve as a reference to guide daily short‐term operations and maintenance tasks, while the long‐term prediction results can inform renovation and reconstruction initiatives for community microgrid. Finally, we choose a community as the subject of our study, and the results indicate that our research can provide new methods for the design and operation of microgrid in smart communities, thereby improving the scalability of the community’s power system.

Automakers are interested in creating optimal car shapes that can visually convey environmental friendliness and safety to customers. This research examined the influence of vehicle form on perceptions based on two subjective inference measures: safety and perceived environmental friendliness (PEF). A within-subjects study was conducted in 2009 (Study 1) to study how people would evaluate 20 different vehicle silhouettes created by designers in industry. Participants were asked to evaluate forms on several scales, including PEF, safety, inspired by nature, familiarity, and overall preference. The same study was repeated in 2016 (Study 2). The results from the first study showed an inverse relationship between PEF and perceptions of safety. That is, vehicles that appeared to be safe were perceived to be less environmentally friendly, and vice versa. Participants in the second study showed a similar trend, but not as strongly as the 2009 participants. Several shape variables were identified to be correlated with participants’ PEF and safety ratings. The changes in the trend of participants’ evaluations over seven years were also discussed. These results can provide designers with insights into how to create car shapes with balanced PEF and safety in the early design stage.

Hybrid robots can assist human workers in various tasks due to their integration of mobility and manipulability. The rapid diffusion of these robots in factories has significantly elevated the automation and intelligence level of manufacturing, while also brings challenges to human–robot collaboration. Traditionally, human workers need to instruct robots to perform a range of tasks by explicitly demonstrating these operations. However, this process imposes excessive burdens on workers as the tasks and environment for robots become more and more diversified and complex. To alleviate this issue, we propose an innovative robotic manipulation framework based on continual knowledge graph embedding. This framework enables hybrid robots to break free from the constraints of fixed rules set by human demonstrations, instead endowing them with inferring capability. The core idea is to utilize semantic information related to objects (such as category, material, and components) and tasks assigned to infer appropriate operational parameters for robots via a knowledge graph. These operational parameters include the suitable type of gripper, the proper area for object manipulation, and the reasonable force range for effective grasping. We conduct an experimental analysis of the proposed framework with a real-world hybrid robot, which performed 158 different tasks involving 46 objects commonly seen in industry, achieving a success rate of up to 96.8%. Furthermore, our framework can continuously enhance the adaptability of robotic operations and effectively balance the learning of new and old knowledge. This research contributes to the development of advanced robotic manipulation method in the context of industrial human–robot collaboration.

Abstract Accurate identification of diagenetic facies is crucial for reservoir characterization, as it directly determines the evaluation of petrophysical properties, pore structure types, and reservoir quality, thereby playing a pivotal role in predicting high-quality hydrocarbon-bearing zones. However, conventional identification approaches are often limited by subjective interpretation, lack of standardized criteria, and low operational efficiency. Meanwhile, existing intelligent classification techniques frequently fail to adequately discriminate subtle but critical data variations, leading to suboptimal classification accuracy. To address these challenges, this paper develops a novel method for diagenetic facies identification based on data difference enhancement (DDE). The proposed methodology consists of three key steps: first, high-dimensional well-logging data are projected into a lower-dimensional feature space using the t-SNE algorithm to improve computational efficiency while preserving nonlinear relationships. Subsequently, the k-means clustering algorithm partitions the processed dataset into distinct groups, thereby amplifying intra-cluster data homogeneity and inter-cluster separability. Next, an ensemble learning architecture is constructed using the stacking algorithm, where cluster-specific meta-classifiers are individually optimized to enhance model robustness. During application, unclassified samples are assigned to their nearest cluster based on Euclidean distance metrics, followed by targeted prediction using the corresponding meta-classifier. The data from the second member of the Upper Triassic Xujiahe Formation are employed for model evaluation, and the results show that the DDE machine learning method significantly outperforms conventional machine learning algorithms, including k-nearest neighbours, support vector machines, and random forests, achieving an accuracy rate of 86.4%. This workflow enables efficient and reliable diagenetic facies classification using standard well-logging curves, offering both theoretical insights and practical tools for reservoir quality prediction in hydrocarbon exploration.