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Karel Čapek’s play R.U.R. (Rossum’s Universal Robots) was a big hit when it was first performed in the 1920s. Written in Czech, it was soon translated into English and other languages and performed throughout Europe and the Anglo-American world. It was quickly adapted for radio and television as well. Through this success, it had a significant influence on technologists, writers, and general audiences. [ABSTRACT FROM AUTHOR]

TThis research aims to comprehensively assess the production facilities, production performance, operating income, the marketing mix, supporting services, and strategy development within Small and Medium-Sized Enterprises (SMEs) in the pastry and bakery industry. The study includes 41 respondents, comprising business owners, seven employees, 30 consumers, and three expert chefs in the pastry and bakery field. A mixed-method approach, combining descriptive qualitative and quantitative analyses, was employed. The research involved both qualitative exploration of respondents' perceptions and quantitative measurements of production performance and marketing effectiveness. Findings revealed satisfactory product quality, yielding profitable results from an average daily production of 100 bread loaves and 32 cakes. While the overall marketing mix was effective, the promotional aspect was identified as an area requiring enhancement. Supporting services were underutilized, suggesting opportunities for improvement, particularly in optimizing raw material utilization to heighten productivity and income while ensuring superior product quality. The study underscores the need for strategic enhancements in promotional efforts and the maximization of supporting services to align with the already satisfactory product quality. Furthermore, it emphasizes the significance of leveraging raw materials effectively to augment productivity and profitability in the SMEs pastry and bakery sector. Enhancing promotional strategies and optimizing supporting services are crucial for SMEs in the pastry and bakery industry to strengthen market positioning. Additionally, future research endeavors should delve deeper into the impact of optimized raw material utilization on productivity and income within this sector, thereby fostering sustained growth and competitiveness.

It is necessary to conduct a study to determine employee perceptions regarding the effect of re-planning the layout of production facilities on company productivity and performance. This will be one of the company's considerations in deciding the implementation of the rearrangement of the layout of its production facilities. This study aims to analyze the measurement model and the relationship between variables used to determine the perceptions of employees of PT. Dua Sahabat Group (a manufacturing company supporting the shipbuilding industry) regarding the effect of re-layout production facilities on company productivity and performance. The relationship between latent variables and manifest variables has been analyzed by using Partial Least Squares-Structural Equation Modeling (PLS-SEM). The test results show that the Composite Reliability (CR) value is above 0.7 and the Average Variance Extracted (AVE) value for the variables X1-X6, Y1 and Y2 is above 0.5, meaning that these variables meet the requirements. The results of this study indicate that the measurement model analysis has good characteristics and meets the requirements, so that it can proceed to the next structural model analysis stage. [ABSTRACT FROM AUTHOR]

Material handling is about availability, movement, and control of material at the right amount, condition, time, sequence, and place. PT. Dua Sahabat Group is a company that is included in the shipbuilding support industry, where currently there is inefficiency in material handling in terms of the layout of production facilities. One of the efforts to increase productivity at PT. Dua Sahabat Group is by reducing the material path distance in the production process, especially for three products, namely ship steel doors, ship cabin doors and ship windows. This study discusses re-planning the layout of production facilities at PT Dua Sahabat Group by using the Systematic Layout Planning (SLP) method. The significance of the research is that the resulting facility layout provides solutions for material movement to support smooth production by shortening the distance between work stations, which means shortening production time and saving costs. The SLP approach has resulted in a decrease in the production line distance of 70.3% for the ship's steel door, 68.4% for the ship's cabin door, and 73.0% for the ship's window. [ABSTRACT FROM AUTHOR]

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Characterized by its resilience, connectivity, and real-time data processing capabilities, the fourth industrial revolution, referred to as Industry 4.0, is the main driver of today's digital transformation. It is crucially important for manufacturing facilities to correctly identify the most suitable Industry 4.0 technologies that meet their operational schemes and production targets. Different technology selection frameworks were proposed to tackle this problem, several of which are complex, or require historic data from manufacturing facilities that might not always be available. The aim of this paper is to develop a novel Industry 4.0 selection framework that utilizes Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) to rank different Industry 4.0 technologies based on their economic, social, and environmental impact. The framework is also implemented on a real-life case study of a manufacturing firm to rank the different Industry 4.0 technologies required for its digital transformation based on their significance to the facility's key performance indicators. The framework is utilized to select the top three Industry 4.0 technologies from a pool of eight technologies that are deemed important to the manufacturing firm. Results of the case study showed that Cyber-Physical Systems, Big Data analytics, and autonomous/industrial robots are the top three ranked technologies, having closeness coefficient scores of 0.964, 0.928, and 0.601, respectively. Moreover, the framework showed sensitivity towards weight changes. This is an advantage in the developed framework, since its main aim is to provide policymakers with a customized list of technologies based on their importance to the firm. [ABSTRACT FROM AUTHOR]

The wave of new industrialization in the Russian Federation, associated with the restoration of a number of industries localized on the territory of the country, as well as the creation of production facilities of a new technological order, required a reassembly of financial and industrial policy and, accordingly, a conceptual rethinking of investment and construction processes. The great importance of cities in solving these problems is undeniable. The dangerous trends of the excessive growth of the service sector and the contraction of the production sector actually nullify the role of cities in solving the priority tasks of the country's development. It becomes obvious that there is a need for the recovery growth of the industrial real estate sector of cities. Despite the fact that the real estate sector is perhaps the most conservative element of the urban environment, that can become a brake on the new industrial wave. In order to prevent this situation, the investment and construction processes of urban development cannot be considered in isolation, they must be predicted, planned and implemented in a synthesized form. The author offers practice-oriented approaches to the synthesis of urban real estate development processes.

Due to the data-driven intelligence from the recent deep learning based approaches, the huge amount of data collected from various kinds of sensors from industrial devices have the potential to revolutionize the current technologies used in the industry. To improve the efficiency and quality of machines, the machine manufacturer needs to acquire the history of the machine operation process. However, due to the business secrecy, the factories are not willing to do so. One promising solution to the abovementioned difficulty is the synthetic dataset and an informatic network structure, both through deep generative models such as differentially private generative adversarial networks. Hence, this article initiates the study of the utility difference between the abovementioned two kinds. We carry out an empirical study and find that the classifier generated by private informatic network structure is more accurate than the classifier generated by private synthetic data, with approximately 0.31–7.66%. [ABSTRACT FROM AUTHOR]

This paper reports measuring, modeling, and determining the optimized air ionic composition of the air at industrial premises to ensure safe living and working conditions for workers. The possibility of using saline solutions with different degrees of concentration to increase the number of negative ions in the airspace, as well as the variability of the air flow rate for the process of ionization of the air of industrial premises, has been investigated. Analysis of experimental data revealed that an increase in the concentration of saline solutions leads to a decrease in the release of the number of air ions into the vapor-air space of the room. It is proved that in order to improve air quality, it is advisable to enable air ionization using an ultrasonic air ion generator and the use of demineralized water. The optimal input parameters established for the ultrasonic installation are: s – distance to the ultrasonic installation, 40 cm; v – airflow rate, 6.00 m/s; and c – concentration of salt water solution, 3.3 %. The result reported here could be used in the design and development of a control system for an ultrasonic generator of air ions of ventilation systems and microclimate systems in order to create the most comfortable high-quality ionized air at industrial premises. To find the optimal mode of operation of the ionization process, a representation procedure for a neural network was applied, which was most accurate to determine the optimal parameters for ionizing the airspace of the working room. Optimization was performed using a Feed Forward Bottle Neck Neural Network (FFBN NN) representation. This approach allows one to determine several optimal conditions for the process under study on the basis of a compromise solution. [ABSTRACT FROM AUTHOR]

The question of how institutional entrepreneurs working on megaprojects successfully enact change in regard to logic practices remains an unsolved problem in megaproject management literature. Building on an in-depth case study of a megaproject in China, we examine how institutional entrepreneurs manage in order to transform a conventional construction logic into a new logic of “mechanization, autoassembling, and factory construction” in steel bridge industry. We present a model of institutional work conducted by entrepreneurs for institutional entrepreneurship in the megaproject platform. Based on this case, we highlight the significance of institutional compatibility and institutional governmentality in implementing the new logic practices. This article contributes to the literature by examining the different forms of institutional work conducted by entrepreneurs with the aim of driving the institutional changes, and exploring the relationship between megaproject management and institutional entrepreneurship. [ABSTRACT FROM AUTHOR]

In this study, a self-learning discrete Jaya algorithm (SD-Jaya) is proposed to address the energy-efficient distributed no-idle flow-shop scheduling problem (FSP) in a heterogeneous factory system (HFS-EEDNIFSP) with the criteria of minimizing the total tardiness (TTD), total energy consumption (TEC), and factory load balancing (FLB). First, the mixed-integer programming model of HFS-EEDNIFSP is presented. An evaluation criterion of FLB combining the energy consumption and the completion time is introduced. Second, a self-learning operators selection strategy, in which the success rate of each operator is summarized as knowledge, is designed for guiding the selection of operators. Third, the energy-saving strategy is proposed for reducing the TEC. The energy-efficient no-idle FSP is transformed to be an energy-efficient permutation FSP to search the idle times. The speed of operations which adjacent are idle times is reduced. The effectiveness of SD-Jaya is tested on 60 benchmark instances. On the quality of the solution, the experimental results reveal that the efficacy of the SD-Jaya algorithm outperforms the other algorithms for addressing HFS-EEDNIFSP. [ABSTRACT FROM AUTHOR]

Continuous authentication (CA) is a promising approach to authenticate workers and avoid security breaches in the industry, especially in Industry 4.0, where most interaction between workers and devices takes place. However, introducing CA in industries raises the following unsolved questions regarding machine learning (ML) models: its precision and performance; its robustness; and the issue about if or when to retrain the models. To answer these questions, this article explores these issues with a proposed supervised versus nonsupervised ML-based CA system that uses sensors, applications statistics, or speaker data collected by the operator’s devices. Experiments show supervised models with equal error rates of 7.28% using sensors data, 9.29% with statistics, and 0.31% with voice, a significant improvement of 71.97, 62.14, and 97.08%, respectively, over unsupervised models. Voice is the most robust dimension when adding new workers, with less than 2% of false acceptance rate even if workforce size is doubled. [ABSTRACT FROM AUTHOR]

The diversification of the defence industry at the backbone and city-forming enterprises of the complex will directly affect the diversification of single-industry towns, the production facilities transformation of the complex and real estate of single-industry towns. The prospects for the development of the city-forming enterprises of the defence industry and related single-industry towns in the context of a decrease in the state defence order seem unfavorable. The diversification of defence industry complex will affect the formation of a high-quality living environment in a single-industry town.The article considers three alternative solutions to the state and status of these objects – their reconstruction, transformation or possible liquidation. It is noted, that it is possible to solve the urgent task of improving the energy efficiency of buildings through the use of both technical and architectural means during the reconstruction of the industrial complex of the defense industry and real estate of a single-industry town.

Acquiring the real-time spatial–temporal information of manufacturing resources holds the promise to enable efficient operation in factory logistics. This article proposes a system architecture using industrial Internet of Things and digital twin technologies to fulfill spatial–temporal traceability and visibility with seamless cyber-physical synchronization for finished goods logistics in the workshop. A long short-term memory network-enabled genetic indoor-tracking algorithm (GITA) is developed to locate product trolleys via a bluetooth low energy technology, with ultra-wideband applied to sample labeling in the training stage. It is enlightened by genetics to achieve self-adapting online for the long-term performance. A feature selection method based on received signal strength indicator is designed to deal with signal multipath fading and streamline the learning process. In addition, the spatial–temporal information obtained is leveraged to activate location-based services that can help promote operational efficiency. Moreover, a real-life case study is carried out in a world-leading computer manufacturer’s factory to illustrate the viability and practicality of the system and methods proposed, with hardware and software developed. By comparison, the GITA shows superiority over existing approaches despite various noises under the manufacturing scenario, attaining a location precision of about 2 m with a 98.12% accuracy. [ABSTRACT FROM AUTHOR]

Directional measurements of over 2600 links in four distinct factories at 28 GHz are used to formulate the path gain and azimuth gain models to allow reliable 90% coverage estimates. A simple theoretical model of path gain, dependent on ceiling and clutter heights, is found to represent path gain across the four factories with 4.4 dB root-mean-square error (RMSE), contrasted with 6.9 dB slope-intercept fit and 8.5–14.9 dB RMSE for 3GPP factory models. The model also did well against 3.5 GHz path loss data collected over 18 MHz bandwidth in one of the factories, with an RMSE of 3.3 dB. In nonline-of-sight (NLOS) conditions, scattering reduces available antenna azimuth gain from nominal value by up to 7.3 dB in 90% of links. Line-of-sight (LOS) blockage by a 1.7 m × 1 m obstacle in factory aisle leads to 7 dB signal reduction, attributed to availability of other paths. It is found that an access point (AP) using 25 dBm transmit power per polarization, with 23 dBi nominal gain and omnidirectional terminals, supporting $2\times $ 2 MIMO in a 400 MHz bandwidth, can provide 130 Mb/s for 90% of factory locations within 50 m. [ABSTRACT FROM AUTHOR]

This paper proposes a combined planning and optimization method that enables a dual-arm robot to lift up and flip heavy plates using crane pulley blocks. The problem is motivated by the low payload of modern collaborative robots. Instead of directly manipulating heavy plates that collaborative robots cannot afford, the paper develops a planner for collaborative robots to operate crane pulley blocks. The planner assumes a target plate is pre-attached to the crane hook. It optimizes dual-arm action sequences and plans the robot’s dual-arm motion that pulls the rope of the crane pulley blocks to lift up the plate. The crane pulley blocks reduce the payload that each robotic arm needs to bear. When the plate is lifted up to a satisfying pose, the planner plans a sliding-pushing motion for one of the robot arms to tumble over the plate while considering force and moment constraints. The article presents the technical details of the planner and several experiments and analysis carried out using a dual-arm robot made by two Universal Robots UR3 arms. The influence of various parameters and optimization goals are investigated and compared in depth. The results show that the proposed planner is flexible and efficient. This paper is motivated by a cleaning process in a factory that produces sewage press machines. The pressboard of sewage press machines could be as heavy as 1000 kg. Human workers need to flip and clean both sides of the board before installing them to the main axis of a sewage machine. Their solution is using a gantry crane. They attach the board to the crane hook using bearing belts, activate the crane to lift up the board. When the board is raised to a satisfying pose, the workers turn the board over by pushing it. Motivated by human workers’ actions, we developed the planner presented in this paper. We assumed crane pulley blocks in the experiments and analysis, but in practice, they may be replaced with electronic ones to improve effort and efficiency. Using the electronic ones will be a sub-problem since there is no need for pulling ropes. The proposed method is expected to help a company’s technicians better judge if they need a heavy payload manipulator or keep their current crane equipment while employing several intelligent collaborative robots to operate them. As a result, it may help to accelerate the upgrade of manufacturing sites while reducing reforming budgets. Note to Practitioners—This paper is motivated by a cleaning process in a factory that produces sewage press machines. The pressboard of sewage press machines could be as heavy as 1000 kg. Human workers need to flip and clean both sides of the board before installing them to the main axis of a sewage machine. Their solution is using a gantry crane. They attach the board to the crane hook using bearing belts, activate the crane to lift up the board. When the board is raised to a satisfying pose, the workers turn the board over by pushing it. Motivated by human workers’ actions, we developed the planner presented in this paper. We assumed crane pulley blocks in the experiments and analysis, but in practice, they may be replaced with electronic ones to improve effort and efficiency. Using the electronic ones will be a sub-problem since there is no need for pulling ropes. The proposed method is expected to help a company’s technicians better judge if they need a heavy payload manipulator or keep their current crane equipment while employing several intelligent collaborative robots to operate them. As a result, it may help to accelerate the upgrade of manufacturing sites while reducing reforming budgets. [ABSTRACT FROM AUTHOR]

This article studies a distributed heterogeneous hybrid flow shop scheduling problem under nonidentical time-of-use electricity tariffs (DHHFSP-NTOU). The makespan and the total electricity charge are considered as the optimization objectives from the view of production and management. The DHHFSP-NTOU considers different processing capabilitie and time-of-use electricity tariffs for each factory. The mixed-integer linear programming (MILP) model of DHHFSP-NTOU is established. To solve the DHHFSP-NTOU, this article proposes an ant colony optimization behavior-based multiobjective evolutionary algorithm based on decomposition (ACO_MOEA/D). A problem-specific ant colony behavior is presented to construct offspring individuals. Eight neighborhoods within the factory and between factories are adopted to improve the quality of the individuals in the archive set. A right-shift movement is used to reduce the electricity charge. A large number of numerical experiments and comprehensive investigations are carried out to test the efficiency and effectiveness of ACO_MOEA/D. The experimental results show that each component (e.g., ant colony behavior, neighborhoods move operators, right-shift movement) contributes to the performance of ACO_MOEA/D. The comparisons with several related algorithms show the superiority of ACO_MOEA/D for solving the DHHFSP-NTOU. Note to Practitioners—From the managers’ insights, the electricity charge is a large cost in the production. The scheduling is an economical approach to reduce the electricity charge. For the time-of-use (TOU) tariffs, the managers can adjust the schedule to reduce the idle time or move some operations to the interval period with a lower electric price. This article studies a distributed heterogeneous hybrid flow shop scheduling problem under nonidentical TOU (UTOU) electricity. This model can be used in many manufacturing enterprises that have several heterogeneous factories. This article proposes an ant colony optimization behavior-based multiobjective evolutionary algorithm based on decomposition (ACO_MOEA/D) to minimize the makespan and the total electricity charge. The ACO_MOEA/D can provide the economy and high-efficiency schedules for practitioners. The computational results confirm its effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

This article deals with a practical distributed hybrid differentiation flowshop scheduling problem (DHDFSP) for the first time, where manufacturing products to minimize makespan criterion goes through three consecutive stages: 1) job fabrication in first-stage distributed flowshop factories; 2) job-to-product assembly based on specified assembly plan on a second-stage single machine; and 3) product differentiation according to customization on one of the third-stage dedicated machines. Considering the characteristics of multistage and diversified processing technologies of the problem, building new powerful evolutionary algorithm (EA) for DHDFSP is expected. To achieve this, we propose a general EA framework called distributed co-evolutionary memetic algorithm (DCMA). It includes four basic modules: 1) dual population (POP)-based global exploration; 2) elite archive (EAR)-oriented local exploitation; 3) elite knowledge transfer (EKT) among POPs and EAR; and 4) adaptive POP restart. EKT is a general model for information fusion among search agents due to its problem independence. In execution, four modules cooperate with each other and search agents co-evolve in a distributed way. This DCMA evolutionary framework provides some guidance in algorithm construction of different optimization problems. Furthermore, we design each module based on problem knowledge and follow the DCMA framework to propose a specific DCMA metaheuristic for coping with DHDFSP. Computational experiments validate the effectiveness of the DCMA evolutionary framework and its special designs, and show that the proposed DCMA metaheuristic outperforms the compared algorithms. [ABSTRACT FROM AUTHOR]

5G-and-beyond (B5G) networks are moving toward the higher end of the millimeter-wave (mmWave) spectrum (i.e., from 25 to 100 GHz) to support integrated communications and ranging (ICAR) services in next-generation factory deployments. The ICAR services in factory deployments require extreme bandwidth/capacity and large ranging coverage, which a mmWave-B5G system can fulfill using massive multi-input and multioutput (mMIMO), beamforming, and advanced ranging techniques. However, as mmWave signal propagation is sensitive to harsh channel conditions experienced in typical indoor factory environments, there is a growing interest in the realistic mmWave indoor channel modeling to evaluate the practical scope of the mmWave-B5G systems. In this article, we study and implement a 3-D stochastic channel model using the baseline third-generation partnership project model. Our channel model employs the time-cluster spatial-lobe (TCSL) technique and utilizes the temporal and spatial statistics to create the channel impulse response (CIR), reflecting realistic indoor factory conditions. Using the generated CIR, we present the performance analysis of an mmWave-B5G system in terms of power delay profile, path loss, communication and ranging coverage, and mMIMO channel capacity. [ABSTRACT FROM AUTHOR]

Sustainability and the circular economy are becoming increasingly important for industry and academia. Learning factories at higher education institutions around the globe attempt to educate students and industrial workers on these topics through specially developed training modules. To successfully implement measures to mitigate issues, such as climate change, product life cycle considerations—in which different phases may require different worker competencies—are indispensable. In this context, we provide some evaluation of learning factory use cases regarding sustainability. The use cases are evaluated on product life cycle phases and respective worker sustainability and circular economy competencies. The results show that the majority of the use cases focus on the production life cycle phase; product development, product use, and end-of-life have either not or very rarely been addressed. Our general conclusions and implications relate to these findings with a recommendation that learning factories expand their training capabilities. [ABSTRACT FROM AUTHOR]

Virtual manufacturing (VM) is an emerging manufacturing practice that is increasingly popular among manufacturers. The success of VM requires an in-depth understanding and innovative applications of operations strategies, supply chain management techniques, and business dynamics. In this article, we identify the critical capabilities of VM firms and their organizational performance implications. We first compiled two lists of potential critical capabilities from the academic and practical perspectives. From the lists, we identified nine and six initial constructs of capabilities and organizational performance measures, respectively. Based on the identified constructs, we designed a structured survey questionnaire and conducted an organizational-level field study. Obtaining 150 valid responses, we analyzed the data by exploratory factor analysis to ascertain the critical capabilities and organizational performance measures and by multiple regression analysis to examine their relationships. The data analysis yielded four critical capabilities, namely technological capability, marketing capability, dynamic capability, and relationship capability, and four organizational performance measures, namely personnel performance, corporate social responsibility performance, relationship performance, and marketing performance. We found that relationship capability was the most prominent among the four critical capabilities. Finally, we conducted a case study of a pioneering VM firm to validate the empirical findings from the practical perspective. [ABSTRACT FROM AUTHOR]

Colombia, is a country with 114 million hectares, and with a potential use of land in agricultural and animal production of more than 40 million hectares, of which today approximately 34 million are dedicated to livestock, nevertheless, according to UPRA only It has a potential vocation of 15 million hectares suitable for livestock farming and the rest hectares in poultry and pig's production, that's why It shows an inadequate use of the potential of the land. According to ICA, by 2023 the livestock population in the country was distributed in 620.807 properties with 29.642.539 animals, which represents an increase of 1.2% compared to 2022, pork production according to the number of animals was 9.658.204, and the bird's population was distributed in 473.961 properties with 215.217.692 animals. Those potential uses of the land have allowed to the livestock production in the country could tend to grow, especially as it has been happening in recent years, in poultry and pork production, in the function of either national or worldwide population, taking into account that by 2050, it is expected to have close to 10 billion inhabitants, which means that there will be a great demand for food, where animal protein there will be very important. In this way, livestock production in the country must have higher standards of efficiency in its production, greater sustainability, and introducing aspects such as animal welfare, beyond what the country has been making. Therefore, the objective of this work is to show the bioclimatic concept and It is application in Livestock Production, understanding this as the relationship between external and internal climatic and environmental variables inside of any animal facility, which allows the generation of a microclimate where the animals are housed, and It could help them to achieve the maximum performance productively. The Bioclimatic as a science has being applied in developed countries, and today technology and innovation based on Precision Livestock Farming - PLF systems are used, in order to have more sustainable production systems. The context of bioclimatic and its application in animal production in Colombia is not widely applied, that's why it's necessary to make major transformations in our production systems, starting with the production facilities and the use of new technologies, in order to take control and make decisions in real time over the microclimatic aerial environments, which presents great challenges for the present and future of the country. [ABSTRACT FROM AUTHOR]

Lot-to-order matching (LTOM) is a crucial process in semiconductor manufacturing since inefficient allocation and order release have strong adverse effects on factory performance. Although prior research proposes several heuristics for the mathematical optimization of the LTOM process, successful real-world implementations following practical and comprehensive approaches are scarce. Our longitudinal case study addresses that issue by summarizing the results of an extensive research project on the automation and optimization of the LTOM process for 200 mm and 300 mm wafers at Infineon Technologies Dresden. Grounded in Action Design Research, we integrated different research methods to provide meaningful insights into the benefits, challenges, and best practices of our approach. Thereby, we also compare the results for 200 mm and 300 mm wafers. The project had positive impacts on multiple quantitative and qualitative key performance indicators, e.g., throughput, on-time delivery, tool utilization, cycle and working time savings, collaboration, and employee satisfaction. Finally, we provide managerial guidance for similar projects and implications for future research. [ABSTRACT FROM AUTHOR]

In this article, a distributed hybrid flow shop scheduling problem with variable speed constraints is considered. To solve it, a knowledge-based adaptive reference points multiobjective algorithm (KMOEA) is developed. In the proposed algorithm, each solution is represented with a 3-D vector, where the factory assignment, machine assignment, operation scheduling, and speed setting are encoded. Then, four problem-specific lemmas are proposed, which are used as the knowledge to guide the main components of the algorithm, including the initialization, global, and local search procedures. Next, an efficient initialization approach is presented, which is embedded with several problem-related initialization rules. Furthermore, a novel Pareto-based crossover heuristic is designed to learn from more promising solutions. To enhance the local search abilities, a speed adjustment local search method is investigated. Finally, a set of instances generated based on the realistic prefabricated production system is tested to verify the efficiency and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle’s initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles’ weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%. [ABSTRACT FROM AUTHOR]

The wide deployment of advanced robots with industrial IoT (IIoT) technologies in smart factories generates a large volume of data during production and a wide variety of data processing workloads are launched to maintain productivity and safety of smart manufacture. The emerging fog computing paradigm offers a promising solution to enhancing data processing performance in a smart factory environment while on the other hand brings in new challenges to resource management, which call for a more effective approach for recommending resource configurations to heterogeneous workloads. In this paper, we propose an Optimized Recommendations of Heterogeneous Resource Configurations (ORHRC) model that employs machine learning techniques to provide resource configuration recommendations for the heterogeneous workloads in a fog computing-based smart factory environment. ORHRC learns a recommendation model by leveraging the operating characteristics and execution time of workloads on fog servers with different configurations. We also design a decision model in ORHRC to further improve prediction accuracy and reduce operational overheads. Experiment results show that ORHRC outperforms the state of art configuration recommendation methods in terms of average prediction accuracy. Note to Practitioners—The various data processing workloads in a smart factory environment need to be processed by the computational resources with optimal configurations for meeting their performance requirements. In this paper, we employ machine learning technologies for enabling automatic recommendation of resource configurations to heterogeneous workloads. Specifically, we develop an Optimized Recommendations of Heterogeneous Resource Configurations (ORHRC) model that can identify the optimal resource configurations for various workloads. We also conducted extensive experiments that verify the effectiveness of the proposed ORHRC model. [ABSTRACT FROM AUTHOR]

In the digital twin environment, the fusion data onto physical entities in the physical space are mapped to multiple virtual spaces for digital modeling and intelligent simulation in different dimensions. In real intelligent manufacturing scenarios, heterogeneous multi-source fusion data are collected at the same time period. So they are consistent in time state. For the autonomous digital twin system, time states verification and integrity checking are basic security factors. Provable data possession technology can check the integrity of data onto virtual spaces. The blockchain can provide the synchronization interface to make distributed entities to obtain the trusted time state value. Considering the privacy, the blockchain can also provide anonymous services for entities. Therefore, we propose the blockchain-based synchronized provable data possession scheme (named BSPDP) for digital twin. In our scheme, the selection of verifier is flexible. Since virtual spaces may be maliciously framed to pay compensation, we use tag verification to prevent honest virtual spaces from being framed. Under the assumption of RSA, the proposed BSPDP is provably secure. Finally, the performance analysis demonstrates that BSPDP is practical. The experimental results show that BSPDP is effective and attractive for digital twin. [ABSTRACT FROM AUTHOR]

In recent years, both distributed scheduling problem and energy-efficient scheduling have attracted much attention. As the integration of these two problems, the distributed energy-efficient scheduling problem is of great realistic significance. To the best of our knowledge, the distributed energy-efficient parallel machines scheduling problem (DEPMSP) has not been studied yet. This article aims to solve DEPMSP by integrating factory assignment and machine assignment into an extended machine assignment to handle the coupled relations of subproblems. A knowledge-based two-population optimization (KTPO) algorithm is proposed to minimize total energy consumption and total tardiness simultaneously. Five properties are derived by analyzing the characteristics of DEPMSP. The population is initialized by using two heuristics based on problem-specific knowledge and a random heuristic. The nondominated sorting genetic algorithm-II and differential evolution perform cooperatively on the population in parallel. Moreover, two knowledge-based local search operators are proposed to enhance the exploitation. Extensive simulation experiments are conducted by comparing KTPO with four algorithms from the literature. The comparative results and statistical analysis demonstrate the effectiveness and advantages of KTPO in solving DEPMSP. [ABSTRACT FROM AUTHOR]

With increasing environmental awareness and energy requirement, sustainable manufacturing has attracted growing attention. Meanwhile, distributed manufacturing systems have become emerging due to the development of globalization. This article addresses the energy-aware distributed hybrid flow-shop scheduling (EADHFSP) with minimization of makespan and energy consumption simultaneously. We present a mixed-integer linear programming model and propose a cooperative memetic algorithm (CMA) with a reinforcement learning (RL)-based policy agent. First, an encoding scheme and a reasonable decoding method are designed, considering the tradeoff between two conflicting objectives. Second, two problem-specific heuristics are presented for hybrid initialization to generate diverse solutions. Third, solutions are refined with appropriate improvement operator selected by the RL-based policy agent. Meanwhile, an effective solution selection method based on the decomposition strategy is utilized to balance the convergence and diversity. Fourth, an intensification search with multiple problem-specific operators is incorporated to further enhance the exploitation capability. Moreover, two energy-saving strategies are designed for improving the nondominated solutions. The effect of parameter setting is investigated and extensive numerical tests are carried out. The comparative results demonstrate that the special designs are effective and the CMA is superior to the existing algorithms in solving the EADHFSP. [ABSTRACT FROM AUTHOR]

Fine-grained and wide-scale connectivity is a precondition to fully digitalize the manufacturing industry. Driven by this need, new technologies such as time-sensitive networking (TSN), 5G wireless networks, and industrial Internet-of-things (IIoT) are being applied to industrial communication networks to reach the desired connectivity spectrum. With TSN emerging as a wired networking solution, converging IT and operational technology (OT) data streams, 5G is upscaling its access and core networks to function as an independent or a transparent TSN carrier in demanding OT use-cases. In this article, we discuss the drivers for future industrial wireless systems and review the role of 5G and its industrial-centric evolution towards meeting the strict performance standards of factories. We also elaborate on the 5G deployment options, including frequency spectrum allocation and private networks, to help the factory owners discern various dimensions of solution space and concerns to integrate 5G in industrial networks. [ABSTRACT FROM AUTHOR]

The Industry 4.0 (I4.0) aims to develop a framework where the new technologies interoperate with each other and with employees, creating a smart and efficient environment. Although there are many public and private initiatives focused on boosting the deployment of I4.0 in all sectors worldwide, the adoption is slower than expected. One of the main reasons is the lack of training in those technologies involved in I4.0, the so-called key-enabling technologies (KET). In this article, the current status of I4.0 adoption from the industry, employees, and training point of view is analyzed. The lack of I4.0 competences in the curricula of vocational education training (VET) and higher education (HE) is also highlighted. Finally, the European innovative training action IN4WOOD is presented as a successful open and free training tool developed to offer students, employees, and managers an easy way to learn, use, and deploy KET of I4.0. Although the main target users of the training action are those in the furniture and woodworking sector, it has been designed to be useful also for users in other business sectors. The training tool is composed of more than 300 video learning pills, practical use cases, gamification, and evaluation test for measuring the level of knowledge acquired. The training tool has been tested in a pilot launched in four European countries. The results from the pilot prove that the IN4WOOD training helps to fill the skill gaps identified in the current VET/HE students and improves the competitiveness of employees, managers, and enterprises. [ABSTRACT FROM AUTHOR]

This article investigates the effect of different temperatures (30 °C, 50 °C, and 70 °C) on the interfacial and electrical insulation properties of 500 kV high voltage direct current (HVDC) cross-linked polyethylene (XLPE) cable factory joint having different roughness levels. The test samples with different interfacial roughness (#80, #400, #1000, #2000, and #0000) were prepared by surface polishing followed by an XLPE injection vulcanization process. At the interface, visible microporous defects were observed. The volume of pits on the sample surface and microporous defects at the interface can be reduced by a smooth interface. Moreover, the smoother interface facilitates space charge accumulation. The dc breakdown strength was nearly the same regardless roughness level at high temperatures. The results show that smooth interfaces help improving insulation properties of cable factory joints at low temperatures. However, the effect of interfacial roughness on insulating properties is weakened at elevated temperatures. [ABSTRACT FROM AUTHOR]

This paper investigates a new multi-objective order assignment and scheduling problem for personal protective equipment (PPE) production and distribution during the outbreak of epidemics like COVID-19. The objective is to simultaneously minimize the total cost and maximize the PPE supply timeliness. For the problem, we first develop a bi-objective mixed-integer linear program (MILP). Then an $\epsilon $ -constraint combined with logic-based Benders decomposition method is proposed based on some explored properties. We then extend the proposed model to handle dynamics and randomness. In particular, we design a predictive reactive rescheduling approach to address random order arrivals and manufacturer disruptions. Computational experiments on a real case from China and 100 randomly generated instances are conducted. Results show that the proposed algorithm significantly outperforms an adapted $\epsilon $ -constraint method combined with the proposed MILP and the widely used non-dominated sorting genetic (NSGA-II) in obtaining high-quality Pareto solutions. Note to Practitioners—The unprecedented outbreak of COVID-19 and its rapid spread caught numerous national and local governments unprepared. Healthcare systems faced a vital scarcity of PPEs. The urgency of producing and delivering PPEs increases as the number of infected cases rapidly increases. A key challenge in response to the epidemic is effectively and efficiently matching the demands and needs. Performing practical and efficient order assignment and scheduling for PPE production during the COVID-19 outbreak is critical to curbing the COVID-19 pandemic. This work first proposes a bi-objective mixed-integer linear program for optimal order assignment and scheduling for PPE production. The aim is to achieve an economical and timely PPE production and supply. A novel method that combines the $\epsilon $ -constraint framework and the logic-based Benders decomposition is proposed to yield high-quality Pareto solutions for practical-sized problems. Computational results indicate that the proposed approaches are practical and feasible, which can help decision-makers to perform acceptable order assignment and scheduling decisions. [ABSTRACT FROM AUTHOR]

With the development of smart manufacturing technology, manufacturers all over the world have begun using automated processing and unmanned factories. China already has many high-end production lines that use intelligent robots, where Artificial Intelligence (AI) and robot vision play a critical role in facilitating robot-driven production. This paper provides a review of the state-of-the-art in Deep Learning (DL) techniques and the applications of DL-based robot vision in smart manufacturing, with an emphasis on the contributions made by China. We conclude by summarizing challenges and prospects in the area. [ABSTRACT FROM AUTHOR]

The need to increase mobility and remove cables in industrial environments is pushing 5G as a valuable communication system to connect traditional deterministic Ethernet-based devices. One alternative is the adoption of Time Sensitive Networking (TSN) standards over 5G Non-Public Networks (5G NPN) deployed in the company premises. This scenario presents several challenges, the most relevant being the configuration of the 5G part to provide latency, reliability and throughput balance suitable to ensure that all the TSN traffic can be delivered on time. Our research work addresses this problem from the perspective of automata learning. Our aim is to learn from the live network to build a smart controller that can dynamically predict and apply a suitable configuration of the 5G NPN to satisfy the requirements of the current TSN traffic. The article presents the main ideas of this novel approach. [ABSTRACT FROM AUTHOR]

Due to the inconsistent imaging environment, the styles of multitemporal multispectral images (MSIs) are quite different, such as image brightness and transparency. For multitemporal MSIs with different styles, the “same object with different spectra” problem is one of the biggest challenges in change detection. To overcome the challenge, a novel unsupervised spatial–spectral feature learning (FL) framework based on style transformation (ST) (called STFL-CD) is proposed for MSI change detection in this article. For dual-temporal MSIs, the proposed STFl-CD algorithm consists of two phases: ST and spatial–spectral FL. Since the image styles are inconsistent under different imaging environments, the first innovation is to transform the image styles through unmixing and reconstruction. Through ST, the challenge of the “same object with different spectra” problem will be reduced fundamentally. By introducing the attention mechanism, the other innovation is to extract the joint spectral–spatial change features based on a 3-D convolutional neural network with spatial and channel attention. In addition, for multitemporal MSIs, a multitemporal version STFL-CD (MT-STFL-CD) framework is designed based on a recurrent neural network to learn the correlation features between multitemporal remote sensing images. Both of the visual and quantitative results on the real MSI datasets indicate that the proposed unsupervised STFL-CD frameworks have significant advantages on multitemporal MSI change detection. In particular, the performance of the proposed unsupervised STFL-CD algorithm is even comparable to that of the state-of-the-art supervised or semisupervised methods. [ABSTRACT FROM AUTHOR]

Different from conventional wired line connections, industrial control through wireless transmission is widely regarded as a promising solution due to its reduced cost, increased long-term reliability, and enhanced reliability. However, mission-critical applications impose stringent quality of service (QoS) requirements that entail ultra-reliability low-latency communications (URLLC). The primary feature of URLLC is that the blocklength of channel codes is short, and the conventional Shannon’s Capacity is not applicable. In this paper, we consider the URLLC in a factory automation (FA) scenario. Due to densely deployed equipment in FA, wireless signal are easily blocked by the obstacles. To address this issue, we propose to deploy intelligent reflecting surface (IRS) to create an alternative transmission link, which can enhance the transmission reliability. In this paper, we focus on the performance analysis for IRS-aided URLLC-enabled communications in a FA scenario. Both the average data rate (ADR) and the average decoding error probability (ADEP) are derived under finite channel blocklength for seven cases: 1) Rayleigh fading channel; 2) With direct channel link; 3) Nakagami-m fading channel; 4) Imperfect phase alignment; 5) Multiple-IRS case; 6) Rician fading channel; 7) Correlated channels. Extensive numerical results are provided to verify the accuracy of our derived results. [ABSTRACT FROM AUTHOR]

In additive manufacturing (AM), factories can quickly adjust their production to meet dynamic object demand without losing profitability, which allows them to implement less complex and more efficient supply chains. To facilitate the adoption of simplified supply chains, we propose the AM Cloud, where a set of micro-manufacturers pool their resources and offer them in an on-demand and pay-per-use basis. However, managing the AM Cloud requires setting a price for objects that incentivizes demand while guaranteeing that the manufacturers can earn a profit. It also requires finding control decisions that minimize operating costs under uncertain object demand. To address these challenges, we first propose a randomized multiobject auction that allocates printing area to the winning buyers and sets prices. We then design an online dynamic control algorithm that finds optimal decisions for the manufacturers to fulfill the object production orders from the auction winners without detailed knowledge of the system statistics. We show that the auction mechanism is truthful, individually rational, and guarantees that the manufacturers can make a profit. We also show that the solution obtained by our dynamic control algorithm is within a tight bound of the optimal one and can fulfill orders within a buyer-defined deadline. [ABSTRACT FROM AUTHOR]

As internet of things and other technologies are widely used in industrial manufacturing, automation and intelligence have witnessed rapid developments, resulting in the proposal of the industrial internet of things (IIoT). However, the IIoT still faces various network security threats; hence, data integrity, confidentiality, and anonymity need to be ensured. The use of cloud and edge servers as semitrusted third parties often results in the leaking of privacy sensitive user data. Meanwhile, existing security schemes treat the cloud and edge as fully trusted entities, which is not always valid. Considering edge servers as semitrusted entities, we propose a novel message authentication scheme that leverages group signature technology and proxy reencryption technology to ensure data integrity, confidentiality, and anonymity. Through theoretical analysis and performance comparison, we prove the security of our scheme. In addition, we implement our scheme on a real publish/subscribe system, and the experimental results show the feasibility of our scheme. [ABSTRACT FROM AUTHOR]

Fog computing is suitable for latency constrained applications useful to end users and IoT devices in smart cities, factories, and homes. A federation among fogs is beneficial for subscribers and providers in terms of enhanced capability, capacity, coverage, and services. To realize such a federation, a third-party authentication mechanism among fog providers is required, so that a subscriber of a fog can access the services provided by the other fogs without having to create new accounts. In this article, we propose a transparent and standard-compliant universal fog proxy that provides third-party authentication among OpenID Connect (OIDC), 802.1x, and Protocol for Carrying Authentication for Network Access (PANA) without requiring a new protocol. The proxy consists of virtual counterparts of the entities involved in these protocols so that it provides transparency. For example, when a fog using OIDC receives an authentication request, the proxy relays and behaves as a virtual Identity Provider (vIdP) for the fog using OIDC and a virtual supplicant for the fog using 802.1x. We applied our solution to nine scenarios across OIDC, 802.1x, and PANA. Experimental results show that the proxy takes 4–52 percent of the total authentication time of 0.128-3.504s for nine scenarios, with a larger percentage in scenarios involving OIDC due to multiple re-directions among virtual components. The scenarios involving 802.1x take a considerably lon-ger time, though a low percentage (4–12 percent) by the proxy, as the spanning tree protocol in an 802.1x switch takes about one second to converge when adding a new device to the network. [ABSTRACT FROM AUTHOR]

Deep learning has recently garnered significant interest in many applications especially for big data analytics in the edge computing environment. Federated learning, as a novel machine learning technique, aims to build a shared learning model from training data on distributed edge nodes to protect data privacy. However, the model update in federated learning requires parameter exchanges among edge nodes, which is rather bandwidth-consuming. This article proposes a novel distributed hierarchical tensor deep computation model by condensing the model parameters from a high-dimensional tensor space into a set of low-dimensional subspaces to reduce the bandwidth consumption and storage requirement for federated learning. Moreover, an updating approach with a hierarchical tensor back-propagation algorithm is developed by directly computing the gradients of low-dimensional parameters to reduce the memory requirement of training for edge nodes and improve training efficiency. Finally, extensive simulations on classical datasets with different local data distributions are presented for the performance evaluation. The results demonstrate that the proposed model relieves the burden of communication bandwidth and reduces energy consumption at edge nodes for federated learning. [ABSTRACT FROM AUTHOR]

Industry 4.0 proposes the use of 5G networks to support intra-factory communications in replacement of current communication practices. 5G networks offer high availability, ultra-low latency, and high bandwidth, and allow the allocation of computational resources closer to the factories for reducing latency and response time. In addition, artificial intelligence can help in making smart decisions to improve the industrial and logistic processes. This work presents an interesting use case that combines Industry 4.0, 5G networks, and deep learning techniques for predicting the malfunctioning of an automatic guided vehicle (AGV) by exclusively using network traffic information and without needing to deploy any meter in the end-us-er equipment AGV and programmable logic controller (PLC). The AGV is connected through a 5G access to its PLC, which is deployed and virtualized in a multi-access edge computing infrastructure. A complete set of intensive experiments with a real 5G network and an industrial AGV were carried out in the 5TONIC environment, validating the effectiveness of this solution. [ABSTRACT FROM AUTHOR]

Precise position information is considered as the main enabler for the implementation of smart manufacturing systems in Industry 4.0. In this article, a time-of-flight based indoor positioning system for LiFi is presented based on the ITU - T recommendation G.9991. Our objective is to realize positioning by reusing already existing functions of the LiFi communication protocol which has been adopted by several vendors. Our positioning algorithm is based on a coarse timing measurement using the frame synchronization preamble, similar to the ranging, and a fine timing measurement using the channel estimation preamble. This approach works in various environments and it requires neither knowledge about the beam characteristics of transmitters and receivers nor the use of fingerprinting. The new algorithm is validated through both, simulations and experiments. Results in an $\text{1}\;{\rm{m}} \times \text{1}\;{\rm{m}} \times \text{2}\;{\rm{m}}$ area indicate that G.9991-based positioning can reach an average distance error of a few centimeters in three dimension. Considering the common use of lighting in indoor environments and the availability of a mature optical wireless communication system using G.9991, the proposed LiFi positioning is a promising new feature that can be added to the existing protocols and enhance the capabilities of smart lighting systems further for the benefit of Industry 4.0. [ABSTRACT FROM AUTHOR]

Industry 4.0 will revolutionize industrial automation. Yet, future smart factories will not be created from scratch. They will rather evolve from existing legacy installations. Consequently, also industrial networks will evolve and the result will be a mixture of new and legacy components. This will make new security mechanisms necessary, that are specifically designed for this industrial use case. This work proposes modifications for MACsec , a new security protocol for protecting communication traffic. These modifications enable MACsec to work within future industrial settings, circumventing drawbacks introduced by legacy networking technologies. Furthermore, we managed to significantly increase the performance of MACsec. [ABSTRACT FROM AUTHOR]

Model-based analysis of production systems is one of the main areas in manufacturing research. The foundation of the successful application of these theoretical studies is the availability of valid and high-fidelity mathematical models that are capable of capturing the behavior of job flow in production systems. The modeling process of a production system, however, may require a significant amount of nonstandardized work that can only be done properly by someone with solid training in the area and extensive experience through real case studies. This poses a critical challenge in the effective implementation of these valuable theoretical results in the Industry 4.0 era. To overcome this, we propose a new production systems modeling paradigm inspired by system identification: calculate production system model parameters that best match the standard system performance metrics measured on the factory floor. Specifically, in this article, we consider production lines characterized by the Bernoulli serial line model and develop algorithms that identify model parameters to fit the system throughput and work-in-process. Analytical algorithms are derived to solve this problem in a two-machine line case and then extended to multi-machine lines. The accuracy and computational efficiency of the algorithms are demonstrated through extensive numerical experiments. Note to Practitioners—A high-fidelity mathematical model is of critical importance to the implementation of any model-based production system analysis method. Currently, the construction of such models is carried out in an ad hoc manner. The quality of the resulting models may heavily depend on the training, experience, intuition, and personal preference of the modeler. The proposed model parameter identification method focuses on standard key performance indices commonly measured on the factory floor. The advantage is twofold. First, these standard performance metrics are consistently defined regardless of industry, thus avoiding any data-ambiguity issue that may occur when using complex machine/equipment status data. Second, measuring these performance metrics in real time is typically convenient and cost effective, even for manufacturing plants without high-end IT infrastructure, thus making the technology accessible to not only large but also small- and mid-sized manufacturers. Using the algorithms developed in this article, a practitioner can quickly construct a serial production line model and then utilize it to access the rich library of production analysis, design, and control methods available in the literature. [ABSTRACT FROM AUTHOR]

Distributed flow shop scheduling of a camshaft machining is an important optimization problem in the automobile industry. The previous studies on distributed flow shop scheduling problem mainly emphasized homogeneous factories (shop types are identical from factory to factory) and economic criterion (e.g., makespan and tardiness). Nevertheless, heterogeneous factories (shop types are varied in different factories) and environment criterion (e.g., energy consumption and carbon emission) are inevitable because of the requirement of practical production and life. In this article, we address this energy-efficient scheduling of distributed flow shop with heterogeneous factories for the first time, where contains permutation and hybrid flow shops. First, a new mathematical model of this problem with objectives of minimization makespan and total energy consumption is formulated. Then, a hybrid multiobjective optimization algorithm, which integrates the iterated greedy (IG) and an efficient local search, is designed to provide a set of tradeoff solutions for this problem. Furthermore, the parameter setting of the proposed algorithm is calibrated by using a Taguchi approach of design-of-experiment. Finally, to verify the effectiveness of the proposed algorithm, it is compared against other well-known multiobjective optimization algorithms including MOEA/D, NSGA-II, MMOIG, SPEA2, AdaW, and MO-LR in an automobile plant of China. Experimental results demonstrate that the proposed algorithm outperforms these six state-of-the-art multiobjective optimization algorithms in this real-world instance. [ABSTRACT FROM AUTHOR]