Search Results (114)

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. Full article

Background: Conventional machine learning courses are usually designed for academic learners, instead of working professionals. This study addresses this gap by proposing a new instructional framework that builds practical computational thinking skills for developing neural network models on business data. Methods: This study proposes a five-component computational thinking framework tailed for working professionals, aligned with the standard data science pipeline and an artificial intelligence instructional taxonomy. The proposed course instructional framework consists of mixed lectures, visualization-driven and coding-driven workshops, case studies, group discussions, and gamified model tuning tasks. Results: Across 28 face-to-face course iterations conducted between 2019 and 2024, participants consistently demonstrated satisfactions in gaining computational-thinking skills. Conclusions: The tailored framework has been implemented to strengthen working professionals’ computational thinking skills for neural-network work on industrial applications. Full article

Dueto uncertainties in real-world production, dynamic factors have become increasingly critical in the research of distributed flexible job shop scheduling problems. Effectively responding to dynamic events can significantly enhance the adaptability and quality of scheduling solutions, thereby improving the resilience of manufacturing systems. This study addresses the dynamic distributed flexible job shop scheduling problem, which involves random job arrivals and machine breakdowns, and proposes an effective discrete improved gray wolf optimization (DIGWO) algorithm-based predictive–reactive method. The first contribution of our work lies in its dynamic scheduling strategy: a periodic- and event-driven approach is used to capture the dynamic nature of the problem, and a static scheduling window is constructed based on updated factory and workshop statuses to convert dynamic scheduling into static scheduling at each rescheduling point. Second, a mathematical model of multi-objective distributed flexible job shop scheduling (MODDFJSP) is established, optimizing makespan, tardiness, maximal factory load, and stability. The novelty of the model is that it is capable of optimizing both production efficiency and operational stability in the workshop. Third, by designing an efficacious initialization mechanism, prey search, and an external archive, the DIGWO algorithm is developed to solve conflicting objectives and search for a set of trade-off solutions. Experimental results in a simulated dynamic distributed flexible job shop demonstrate that DIGWO outperforms three well-known algorithms (NSGA-II, SPEA2, and MOEA/D). The proposed method also surpasses completely reactive scheduling approaches based on rule combinations. This study provides a reference for distributed manufacturing systems facing random job arrivals and machine breakdowns. Full article

The integration of Artificial Intelligence into industrial maintenance remains challenging due to the scarcity of high-quality data representing faulty conditions. Machine Learning models trained on laboratory testbed data often fail to generalize effectively in real workshop environments. This study evaluated the effectiveness of Transfer Learning models in handling this domain shift challenge compared with Machine Learning models. Their potential to address the generalization gap was assessed by analyzing the model adaptability from lab-recorded data to data from emulated workshop conditions, where real-world variability was replicated by embedding synthetic noise into the lab-recorded data. The case study focuses on detecting rotor unbalance through bearing vibration signals at varying speeds. A Support Vector Classifier was trained on the transformed features for both models for binary classification. Model performance was assessed under varying data availability and noise conditions to evaluate the impact of these factors on classification accuracy, sensitivity, and specificity. The results show that Transfer Learning outperforms Machine Learning, achieving up to 30% higher accuracy under high-noise conditions. Although the Machine Learning model exhibits greater sensitivity, it misclassifies balanced cases and reduces specificity. In contrast, the Transfer Learning model maintains high specificity but has difficulty detecting mild unbalance levels, particularly when data availability is limited. Full article

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. Full article

In view of the Flexible Job-shop Scheduling Problem (FJSP) under multi-product and variable-batch production modes, this paper presents an intelligent scheduling approach based on a heterogeneity-enhanced graph neural network combined with deep reinforcement learning. By constructing a heterogeneity-enhanced incidence graph to dynamically represent the scheduling state, the proposed method effectively captures both the dependencies among operations and the interaction features between operations and machines. Moreover, the Proximal Policy Optimization (PPO) algorithm is leveraged to achieve end-to-end optimization of scheduling decisions. Specifically, the FJSP is formulated as a Markov Decision Process. A heterogeneous enhanced graph neural network architecture is designed to extract deep features from operation nodes, machine nodes, and their heterogeneous relationships. Then, a policy network generates joint actions for operation assignment and machine selection, while the PPO algorithm iteratively refines the scheduling policy. Finally, the method is validated in an aerospace component machining workshop scenario and the benchmark dataset. Experimental results demonstrate that, compared with traditional dispatching rules and existing deep reinforcement learning techniques, the proposed approach not only achieves superior scheduling performance but also maintains an excellent balance between response efficiency and scheduling quality. Full article

The air traffic growth expected for future years will likely cause an imbalance between traffic demand and available capacity. This could lead to increased airspace congestion, heightened complexity, and a higher workload for controllers attempting to manage the situation. Nowadays, available tools can identify 4D Area of Relatively High Air Traffic Control Complexity (4DARHAC) events up to 20 min before they occur. Nonetheless, state-of-the-art Artificial Intelligence applications can significantly increase this prediction horizon. Powered by a combination of different Machine Learning models, the ASTRA solution aims to both detect and provide resolution strategies for 4DARHACs up to 1 h before onset. To validate ASTRA’s operational concept, a series of workshops and interviews with Flow Management Position operators were conducted, focusing on assessing the initial concept and identifying end user needs. The feedback collected was validated by a board of Subject Matter Experts (SMEs) and transformed into a concrete set of functional and non-functional requirements. Overall, ASTRA’s operational concept was endorsed as a promising solution for reducing airspace complexity while alleviating operator workload during the tactical phase of operations. Experts further highlighted the importance of integrating ASTRA with existing Flow Management Position software tools to maximize its operational impact and facilitate adoption. Full article

Urban mass tourism and rural depopulation increasingly threaten heritage sites worldwide, leading to socio-economic and environmental challenges. This study adopts a holistic approach to sustainable tourism planning by examining 84 cultural and natural heritage sites in and around Safranbolu and Amasra, two cities in Türkiye that are listed on the UNESCO World Heritage List and the Tentative List. Inspired by historical travelers’ itineraries, it proposes an eco-cultural tourism route to create a resilient heritage network. A participatory methodology integrates charettes within Erasmus+ workshops, crowdsourcing, various analysis methods while engaging stakeholders, and AI-powered clustering for route determination. The study follows a four-stage framework: (1) data collection via collaborative GIS, (2) eco-cultural route development, (3) stakeholder participation for inclusivity and viability, and (4) assessments and recommendations. Results highlight the strong potential of heritage assets for sustainable tourism while identifying key conservation risks. Interviews and site analysis underscore critical challenges, including the absence of integrated site management strategies, insufficient capacity-building initiatives, and ineffective participatory mechanisms. Moreover, integrating GIS-based crowdsourcing, machine learning clustering, and multi-criteria decision-making can be an effective planning support system. In conclusion, this study enhances the sustainability of heritage and tourism by strengthening participatory eco-cultural development and mitigating mass tourism and abandonment’s negative impacts on the heritage sites. Full article

In the era of Industry 5.0, human-centric manufacturing necessitates deep integration between workers and intelligent workshop scheduling systems. However, the inherent variability in worker efficiency due to learning and forgetting effects poses challenges to human–machine–logistics collaboration, thereby complicating multi-resource scheduling in smart workshops. To address these challenges, this study proposes a real-time task-driven human–machine–logistics collaborative framework designed to enhance multi-resource coordination in smart workshops. First, the framework incorporates a learning-forgetting model to dynamically assess worker efficiency, enabling real-time adjustments to human–machine–logistics resource states. Second, a task-driven self-organizing approach is introduced, allowing human, machine, and logistics resources to form adaptive groups based on task requirements. Third, a task slack-based matching method is developed to facilitate real-time, adaptive allocation of tasks to resource groups. Finally, the proposed method is validated through an engineering case study, demonstrating its effectiveness across different order scales. Experimental results indicate that, on average, completion time is reduced by no less than 10%, energy consumption decreases by at least 8%, and delay time is reduced by over 70%. These findings confirm the effectiveness and adaptability of the proposed method in highly dynamic, multi-resource production environments. Full article

Modern product manufacturing is not only becoming more advanced but also requires increasingly precise and technologically advanced solutions, especially in the production process. One example is the automotive industry, where customization is becoming a key requirement. This work aimed to analyze the factors determining the efficiency of production processes, using the example of a selected company from the automotive industry—the production of spare parts—and to assess the impact of the applied optimization tools and techniques on improving operational results. This work combines theoretical and practical aspects, presenting a detailed analysis of data and actions taken in a real production environment. As part of the research, a thorough research program was presented, including the analysis of production data before and after conducting optimization workshops. Before the workshop, key problems were identified, such as the time-consuming rearranging of machines. The analysis using the parametric Student’s t test for two subsidiaries showed the rightness of the optimization activities. During the workshop, several changes were implemented, including the use of a new Destacker, modification of conversation procedures and training operators. The data collected after the workshop indicated a significant reduction in the times of reliance, which confirmed the effectiveness of the activities used. The analysis used tools such as the Pareto diagram and the ABC method, which allowed the identification of priority areas to improve. This work proves that the use of appropriate management tools and employee involvement in the optimization process can significantly improve the efficiency of production processes. Key success factors included the elimination of losses resulting from inefficient procedures, improvement of work organization and implementation of technological solutions. The results of this analysis form the basis for further research on improving production processes in the automotive industry. Full article

During industrial production, a significant amount of oil mist is generated, posing health risks because it is a fine particulate matter that is easily inhaled by the human body. Electrostatic collection has been widely applied in machining workshops as an effective method for capturing oil mist. While existing research has made substantial progress in improving the collection efficiency of electrostatic methods, the challenge of achieving high-efficiency oil mist collection remains unresolved. The inherent physical properties of oil mist contribute to difficulties in its efficient collection. Additionally, the deposition characteristics of oil mist, as well as the structure and operational parameters of the electrostatic precipitator (ESP), directly affect collection efficiency. This paper reviews the literature from the past decade, introduces the mechanisms of oil mist generation, and presents oil mist monitoring technologies. Based on the deposition characteristics of oil mist, it explores high-efficiency collection technologies using electrostatic methods and summarizes studies on optimizing the performance of electrostatic precipitators. Finally, the paper provides an outlook on the development prospects of oil mist purification technologies. Full article

To be successful in a circular economy, it is important to keep the cost of operationalizing remanufacturing processes low in order to retain as much value of the product as possible. Optimizing operations for disassembly, as a key process step, is therefore an important prerequisite for economically viable circular manufacturing. The generation of fit-to-resource disassembly instructions is labor-intensive and challenging because (digital) product information is often lacking at End-of-Life. With upcoming EU regulations for Eco-design for Sustainable Products in mind, including the future use of Digital Product Passports, it is time to think about standardized methods to capture disassembly information for products. First requirements from small and medium-sized remanufacturing companies have been collected and compared with available frameworks for modeling product topology, parameters, and (dis)assembly process rationale. Based on this, the disassembly hypergraph is presented as a concept for recording ‘resource-agnostic disassembly guides’ in (machine-readable) product models to determine required disassembly actions and tools ‘smartly’. The concept builds upon existing models. Additionally, suitable methods for the collection of disassembly information are explored, resulting in preliminary insights from disassembly data collection workshops. Although the approach is promising, future work is needed to expand the concept of the disassembly hypergraph with both guidelines for setting up disassembly ontologies and further systematic disassembly knowledge extraction in order to apply this as a useful means for companies to rationalize their disassembly operations. Full article

Decision support systems (DSSs) can improve decision making in integrated pest management (IPM), but are still underutilised despite proven environmental and economic benefits. To overcome the barriers to DSS adoption, this study analyses survey data from 31 farmers and 94 farm advisors, researchers and developers across 11 European countries. Using machine learning techniques, respondents were first categorised into clusters based on their responses to the questionnaire. The clusters were then explained using classification trees. For each cluster, customised approaches were proposed to overcome the barriers to DSS adoption. For farmers, these include building trust through co-development, offering free trials, organising practical workshops and providing clear instructions for use. For farm advisors and researchers, involvement in the development of DSS and giving them access to information about the characteristics of the DSS is crucial. IPM DSS developers should focus on 14 key recommendations to improve trust and the ease of use, increase the transparency of DSS descriptions and validation, and extend development to underserved sectors such as viticulture and vegetable farming. These recommendations aim to increase the uptake of DSSs to ultimately improve the implementation of IPM practises and help reduce the risk and use of pesticides across Europe despite the ever-growing challenges in agriculture. Full article

The aim of the work was to investigate the influence of the machining parameters on the surface roughness and tool wear during slot milling of a polyurethane block (PUB). In the experiment, the influence of the cutting speed, the feed per tooth and the depth of cut on the roughness Ra and Rz of the milling slot surface and wear of the end mill was analyzed. A three-axis CNC milling machine Emco Concept Mill 55 was used to perform the study. After the machining, the values of parameters Ra and Rz were measured using the Hommel Tester T500 induction profilometer. Three polyurethane materials of different densities were considered: the Labelite 45, the Prolab 65 and the LAB 1000. The wear of the end mill was also examined for each of the tested materials by a workshop microscope. In conclusion, it was indicated how and to what extent the variation in the machining parameters affects the surface geometrical structure of a polyurethane plate. Moreover, the research results for the tested materials were compared with each other. Full article

Artificial Intelligence (AI) has the disruptive potential to transform patients’ lives via innovations in pharmaceutical sciences, drug development, clinical trials, and manufacturing. However, it presents significant challenges, ethical concerns, and risks across sectors and societies. AI’s rapid advancement has revealed regulatory gaps as existing public policies struggle to keep pace with the challenges posed by these emerging technologies. The term AI itself has become commonplace to argue that greater “human oversight” for “machine intelligence” is needed to harness the power of this revolutionary technology for both potential and risk management, and hence to call for more practical regulatory guidelines, harmonized frameworks, and effective policies to ensure safety, scalability, data privacy, and governance, transparency, and equitable treatment. In this review paper, we employ a holistic multidisciplinary lens to survey the current regulatory landscape with a synopsis of the FDA workshop perspectives on the use of AI in drug and biological product development. We discuss the promises of responsible data-driven AI, challenges and related practices adopted to overcome limitations, and our practical reflections on regulatory oversight. Finally, the paper outlines a path forward and future opportunities for lawful ethical AI. This review highlights the importance of risk-based regulatory oversight, including diverging regulatory views in the field, in reaching a consensus. Full article