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Disabled people can be successfully employed in most production processes, provided that one knows how to exploit their abilities and take into account their limitations in order to give them an appropriate job. However, because the level and type of production must be constantly adapted to the needs of the market, the involvement of disabled people in the production process may also change. Additionally, people with disabilities have limitations as well as additional rights that must be considered. As a result, the organisation and planning of their work, side by side with other employees, becomes more complex. Computer simulations can be a support for organising and planning the involvement of employees with disabilities in production processes. The aim of the article is to show how simulations can facilitate the organisation of work of employees with disabilities, with the changing demand for manufactured products. The paper identifies the factors that should be considered, and then presents how the employment of disabled people can affect the operation of the production line and the commercial image of the company. The study uses a combination of System Dynamics and Discrete Event Simulations. The relevant data for the simulation were derived from a production company. [ABSTRACT FROM AUTHOR]

Manufacturers experience random environmental fluctuations that influence their supply and demand processes directly. To cope with these environmental fluctuations, they typically utilise operational hedging strategies in terms of pricing, manufacturing, and procurement decisions. We focus on this challenging problem by proposing an analytical model. Specifically, we study an integrated problem of procurement, manufacturing, and pricing strategies for a continuous-review make-to-stock system operating in a randomly fluctuating environment with exponentially distributed processing times. The environmental changes are driven by a continuous-time discrete state-space Markov chain, and they directly affect the system's procurement price, raw material flow rate, and price-sensitive demand rate. We formulate the system as an infinite-horizon Markov decision process with a long-run average profit criterion and show that the optimal procurement and manufacturing strategies are of state-dependent threshold policies. Besides that, we provide several analytical results on the optimal pricing strategies. We introduce a linear programming formulation to numerically obtain the system's optimal decisions. We, particularly, investigate how production rate, holding cost, procurement price and demand variabilities, customers' price sensitivity, and interaction between supply and demand processes affect the system's performance measures through an extensive numerical study. Furthermore, our numerical results demonstrate the potential benefits of using dynamic pricing compared to that of static pricing. In particular, the profit enhancement being achieved with dynamic pricing can reach up to 15%, depending on the problem parameters. [ABSTRACT FROM AUTHOR]

In a multi-state manufacturing system, preventive maintenance is performed on other machines once the performance of a machine falls below a threshold state. Many investigations have been performed on the maintenance of manufacturing systems. However, studies on preventive maintenance for manufacturing systems that account for the different costs involved are limited. This paper focuses on the analysis of multi-state machines in a manufacturing system considering different costs. Subsequently, a cost-based preventive maintenance prioritisation method for multi-state machines is proposed. Based on the buffer capacity, three maintenance policies on the machines are discussed. Different machine failures lead to different maintenance priorities. The real-time buffer capacity greatly influences the selection of machines for preventive maintenance. The set of the optimal machines for preventive maintenance is determined based on the constraints of machine running time and different preventive maintenance costs. Finally, a numerical example is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The relevance of the subject matter is conditioned by the high-efficiency use of reconfigurable manufacturing systems during construction work and the associated need to develop and implement technological machines with replaceable working bodies. The purpose of this study is to analyze the invariance of the equipment used by types of work in landscape and landscape construction. The study used theoretical methods of analysis and synthesis of information on the principles of the practical application of a universal machine with a wide range of mounted technological working bodies. The results obtained indicate the high relevance of the issue of the rapid changeability of mounted technological working bodies. Its effective solution lies in the creation of reconfigurable production machines based on a universal wheelbase with replaceable working bodies of variable layout by type of work, for which a new design of an inter-module basing and fastening device is proposed. The possibility of its practical application in the BobCat loader system is considered. It is established that, in general, the use of the BobTach mounting frame during various technological operations ensures an increase in the efficiency of this technological equipment. In addition, it significantly increases the performance of the entire model range of equipment and testifies to the versatility of the specified mounting frame. The practical significance of the results obtained is conditioned by the possibility of their application in the development and implementation of modern technological machines with rapidly replaceable working bodies in the agricultural sector and road construction. [ABSTRACT FROM AUTHOR]

Advanced manufacturing is challenging engineering perceptions of how to innovate and compete. The need for manufacturers to rapidly respond to changing requirements and demands; obtain, store, and interpret large volumes of data and information; and positively impact society and our environment requires engineers to investigate and develop new ways of making products for flexible and competitive production. In addition to the associated operational, technological, and strategic advantages for industry, advanced manufacturing creates educational, workforce, and market opportunities. Thus, this literature review aims to investigate the current state and emerging trends in advanced manufacturing. Specifically, this study addresses advances in manufacturing from manufacturing systems perspective, concentrating on emerging trends in process sensing and monitoring, equipment control and automation, machine tools, sustainable manufacturing, and green supply chain management. This review finds myriad efforts have been undertaken by researchers in industry, academia, and government labs from around the world, which have supported the development and implementation of new process technologies to improve manufacturing systems extending from unit process and shop floor operations to facility and supply chain management activities. However, emerging global challenges remain in various domains including energy (e.g., resource scarcities and global warming), critical materials vulnerable to supply disruptions due to crisis and rapid changes in demand, and services (e.g., healthcare supply chains during COVID-19 pandemic). Thus, manufacturing industry must continue the innovative development of advanced materials, manufacturing processes, and systems that ensure cost efficient, rapidly flexible, high quality, and responsible production of goods and services. [ABSTRACT FROM AUTHOR]

Industry increasingly focuses on data-driven digital twins of production lines, especially for planning, controlling and optimising applications. However, the lack of open data on manufacturing systems presents a challenge to the development of new data-driven strategies. To fill this gap, the paper aim to introduce a strategy for generating random production lines and simulating their behaviour, thus enabling the generation of synthetic data. So far, such data can be recorded in event logs or machine status format, with the latter adopted for the use cases. To do so, the production lines are modelled using complex network concepts, with the system's behaviour simulated via an algorithm in Python. Three use cases were assessed, in order to present possible applications. Firstly, the stabilisation of working, starved and blocked machines was investigated until a steady state was reached. The system behaviour was then investigated for different model parameters and simulation intervals. Finally, the production bottleneck behaviour (a phenomenon that can harm the production capacity of manufacturing systems) was statistically studied and described. The authors anticipate that this artificial and parametric data benchmark will enable the development of data-driven techniques without prior need for a real dataset. [ABSTRACT FROM AUTHOR]

A well-determined manufacturing system helps organisations achieve a desired competitive advantage. So, the manufacturing system selection concerns the critical decision of manufacturing strategy deployment. In the present era of industry 4.0, there exist four traditional manufacturing systems (TMS) (i.e. job-shop, batch-shop, mass, and continuous) and additive manufacturing system (AMS). Different process choice criteria (PCC) are responsible for selecting the best-suited system from five or a hybrid (AMS + TMS) configuration. This research formulates a two-stage framework comprising Delphi and Voting analytical hierarchy process (VAHP) techniques for quantifying the congruence between PCC and manufacturing systems. Initially, extant literature is reviewed to identify the critical PCC and further validated by industry experts through Delphi. This results in retaining thirty-six PCC. The relative importance of an individual criterion concerning a particular manufacturing system is computed using VAHP in the second stage to understand the alignment of PCC in different manufacturing systems for exhibiting the congruence between PCC and manufacturing system (TPS and AMS). The findings offer critical insights about the different PCC and their level-of-fit in TMS and AMS, which can assist researchers and practitioners in evaluating a suitable manufacturing system for an organisation using identified PCC. [ABSTRACT FROM AUTHOR]

Storage systems and warehouse processing in factories are integral to manufacturing operations and logistics procedures. Proper factory layout, storage locations, and procedures save time and reduce cost and wastes. This study examines a real case scenario of a metal industry and sheet metal factory manufacturing commercial easy-to-install metal racks. We collected data, investigated the current situation of the factory, and specified the difficulties and problems regarding the procedures, storage system, and distribution of machines. Furthermore, several proposed solutions were presented and analyzed using simulation, followed by the selection of the best solution. Then, we applied 5S methodology to improve productivity and reduce waste. The findings reveal that reducing waste and time, enhancing productivity, and better space utilization can be achieved significantly. Moreover, this study indicates the feasibility of the proposed solutions, which can be adopted by similar factories and other industries.

Innovations and advancements in technology create new opportunities to run and maintain manufacturing plants, which we refer to as digitalised manufacturing. This development is recognised as a socio-technical system (STS) change, where a change in the production system's goals, technology, processes, people, or environment may lead to ripple effects between those sub-systems. Despite this, technology development and technology use cases account for most of the research within digitalised manufacturing, while little attention has been devoted to leadership practices considering digitalised manufacturing from a socio-technical perspective. This paper focuses on the maintenance organisation, whose mission in a company is to keep production systems functional. We aim to describe leadership in industrial maintenance from an STS perspective. This is a unique interview study where twenty maintenance managers from Swedish manufacturing industry offer their perspective on the changing leadership within maintenance, providing a unique insight into the challenges facing leaders of maintenance in digitalised manufacturing. We frame the empirical findings using an STS framework and propose an overall consideration model for leadership that supports the development of a functional maintenance organisation in the face of pervasive digitalisation. [ABSTRACT FROM AUTHOR]

Finite-State Automata (FSA) are foundations for modelling, synthesis, verification, and implementation of controllers for manufacturing systems. However, FSA are limited to represent emerging features in manufacturing, such as the ability to recognise and switch contexts. One option is to enrich FSA with parameters that carry details about the manufacturing, which may favour design and control. A parameter can be embedded either on transitions or states of an FSA, and each approach defines its own modelling framework, so that their comparison and integration are not straight-forward, and they may lead to different control solutions, modelled, processed and implemented distinctly. In this paper, we show how to combine advantages from parameters in manufacturing the modelling and control. We initially presenta background that allows to understand each parameterisation strategy. Then, we introduce a conversion method that translates a design-friendly model into a synthesis-efficient structure. Finally, we use the converted models is synthesis, highlighting their advantages. Examples are used throughout the paper to illustrate and compare our results and tooling support is also provided. [ABSTRACT FROM AUTHOR]

Purpose – Model-based systems engineering (MBSE) is an important approach for the transforming process from “document-centered” to “model centered” systems engineering mode in equipment development, which can effectively shorten the equipment development cycle and improve product design quality. This paper aims to understand if MBSE enables to support manufacturing and equipment development. Design/methodology/approach – The paper opted a bibliometric analysis of MBSE in domain of advanced manufacturing from different perspectives such as publication volume, research team, sources and keyword co-occurrence. Findings – Firstly, the application of MBSE in advanced manufacturing can be roughly divided into three stages. And MBSE has been widely implemented globally and has gradually formed several noteworthy teams. Secondly, this article has identified some high-quality sources, with a large number of publications and citations, the most influential publications focus on the practice or guidance of digital twins and intelligent manufacturing. Thirdly, research can be divided into six categories, including systems engineering, digitalization, intelligent manufacturing, product design, model and architecture and MBSE applications. Research limitations/implications – Because of the chosen research approach, the visualized network tends to lose certain information such as a few keywords may be inaccurately categorized. Practical implications – This paper comprehensively study the research status of MBSE in advanced manufacturing and forecasts future research trends, emphasizing the combination of intelligent manufacturing and digitization. Originality/value – This paper fulfills an identified need to understand the current application status and future development trends of MBSE.

Storage systems and warehouse processing in factories are integral to manufacturing operations and logistics procedures. Proper factory layout, storage locations, and procedures save time and reduce cost and wastes. This study examines a real case scenario of a metal industry and sheet metal factory manufacturing commercial easy-to-install metal racks. We collected data, investigated the current situation of the factory, and specified the difficulties and problems regarding the procedures, storage system, and distribution of machines. Furthermore, several proposed solutions were presented and analyzed using simulation, followed by the selection of the best solution. Then, we applied 5S methodology to improve productivity and reduce waste. The findings reveal that reducing waste and time, enhancing productivity, and better space utilization can be achieved significantly. Moreover, this study indicates the feasibility of the proposed solutions, which can be adopted by similar factories and other industries. [ABSTRACT FROM AUTHOR]

With the integration and advancement of new generation information technology and manufacturing processes, big data in manufacturing presents solutions for transforming manufacturing model today. In order to mine the hidden knowledge value and potential capabilities of big data and facilitate the intelligent decision-making of business managers in complex dynamic manufacturing environments, a comprehensive study of intelligent manufacturing management driven by big data technologies was hereby carried out. Firstly, sources and characteristics of big data in the workshop were explored, providing an overview of the enabled big data technology including data collection, transmission, storage, computation, processing, visualization, and big data mining algorithms. Then, big data application scenarios were reviewed using the intelligent manufacturing workshop as the research context, and applications of big data in furniture workshops were discussed from four major aspects, job shop scheduling, quality control, prediction maintenance, energy management, and supply chain planning. Afterwards, data challenges for furniture intelligent manufacturing in the workshop were clarified, and a conceptual framework for furniture intelligent manufacturing was finally proposed based on big data technologies. Overall, the present research offers valuable insights and ideas for furniture workshops and future research directions. [ABSTRACT FROM AUTHOR]

Under the auspice of further developing 5G mobile communication technology and integrating it with the latest advancements in the field of Industrial Internet-of-Things, this study conducts in-depth research and detailed analysis on the combination of 5G with industrial systems based on composite structures, communication network architectures, wireless application scenarios, and communication protocols. The status quo, development trend, and necessity of 5G mobile communication technology are explored and its potential in industrial applications is analyzed. Based on the current practical development level of 5G technology, by considering different requirements for bandwidth, real-time performance, and reliability in communication networks of industrial systems, this study proposes three feasible paths for the integration between 5G and industrial systems, including the method to use 5G in place of field buses. Finally, by introducing real-world cases, this study has successfully demonstrated the integration of 5G and industrial systems by extending 5G terminals as field bus gateways. This study provides valuable references for research and practice in related fields. [ABSTRACT FROM AUTHOR]

The business environment is becoming increasingly fast-paced, competitive and demanding, making it vital for manufacturing processes to create agile, flexible and simplified environments. The objective of this research is to apply a new entropic and hybrid metric to measure complexity in manufacturing systems with flow shop and job shop environments. The methodological approach is based on equations that facilitate entropic analysis in different types of scenarios and provide quantitative support for decision making. The model is defined in two vectors, one of a classical subjective type based on the complexity index method (CXI), and the other of an objective type focused on Shannon's entropy metric. In both situations a new entropic metric of complexity measurement is applied. For its application, the following are used two particular case studies are used for its application and finally the results and discussions are presented. The findings allow us to solve, with the new method, the shortcomings found in the classical methods and to propose improvement bets in the areas detected in the different processes. [ABSTRACT FROM AUTHOR]

Teaching in higher education is often challenging for the lack of practical implementation and difficulties in student involvement. In engineering classes, students are often deeply involved in computer laboratories and projects in which they are challenged with decision-making problems. The lack of the real system that is being modelled may hinder the effectiveness of the teaching activities. In this paper, we propose a new teaching approach based on the student's interaction with lab-scale models of manufacturing systems. Students have the possibility to make observations, collect data, and implement improvements to a system, all within a course duration. The flexibility of the proposed approach enables its application to a wide range of courses, for instance manufacturing system engineering, production management, Industry 4.0. As case study, we target a course on simulation of manufacturing systems for industrial and mechanical engineering, in which students are asked to build, validate, and use a discrete event simulation model of a production system. The application of this project methodology changed the way of teaching simulation in the course and significantly improved students' evaluation and satisfaction. [ABSTRACT FROM AUTHOR]

In the dynamic realm of manufacturing, it is essential to optimize production processes to attain efficiency and competitiveness. This study presents an innovative enhanced dragonfly optimization (EDFO) method to improve production by utilizing a diverse strategy that combines swarm intelligence and virtual cell development. The suggested methodology includes the parallel EDFO algorithm, which is a cutting-edge variety of swarm intelligence, to address the intricate optimization difficulties related to virtual cell creation. The virtual cell construction process entails the consolidation of machines into cells to optimize output and reduce manufacturing lead times. The benchmark test results offer valuable insights into the algorithm's capabilities and effectively demonstrate its effectiveness in optimizing virtual cell generation for various manufacturing conditions. The proposed approach, which simultaneously takes numerous essential characteristics, is a comprehensive solution for improving production efficiency in virtual cellular manufacturing systems due to its multifunctional nature.

Efficient performance evaluation methods are needed to design and control production systems. We propose a method to analyse single-server open queueing network models of manufacturing systems composed of delay, batching, merge and split blocks with correlated interarrival and service times. Our method (SLQNA) is based on using a supervised learning approach to determine the mean, the coefficient of variation, and the first-lag autocorrelation of the inter-departure time process as functions of the mean, coefficient of variation and first-lag autocorrelations of the interarrival and service times for each block, and then using the predicted inter-departure time process as the input to the next block in the network. The training data for the supervised learning algorithm is obtained by simulating the systems for a wide range of parameters. Gaussian Process Regression is used as a supervised learning algorithm. The algorithm is trained once for each block. SLQNA does not require generating additional training data for each unique network. The results are compared with simulation and also with the approximations that are based on Markov Arrival Process modelling, robust queueing, and G/G/1 approximations. Our results show that SLQNA is flexible, computationally efficient, and significantly more accurate and faster compared to the other methods. [ABSTRACT FROM AUTHOR]

Developing efficient performance evaluation methods is important to design and control complex production systems effectively. We present an approximation method (SLQNA) to predict the performance measures of queueing networks composed of multi-server stations operating under different service disciplines with correlated interarrival and service times with merge, split, and batching blocks separated with infinite capacity buffers. SLQNA yields the mean, coefficient of variation, and first-lag autocorrelation of the inter-departure times and the distribution of the time spent in the block, referred as the cycle time at each block. The method generates the training data by simulating different blocks for different parameters and uses Gaussian Process Regression to predict the inter-departure time and the cycle time distribution characteristics of each block in isolation. The predictions obtained for one block are fed into the next block in the network. The cycle time distributions of the blocks are used to approximate the distribution of the total time spent in the network (total cycle time). This approach eliminates the need to generate new data and train new models for each given network. We present SLQNA as a versatile, accurate, and efficient method to evaluate the cycle time distribution and other performance measures in queueing networks. [ABSTRACT FROM AUTHOR]

Recent push towards sustainable development have lead machine tool builders to begin adopting novel business models which are usually peculiar in product-service-systems. In particular, business models as 'pay-per-use' introduced the issue of rental of production capacity integrated with the management of the life cycle of assets in a Circular Economy perspective. However, when it comes to machine tool builders, the high degree of customization of capital goods and in particular of multi-stage automated assembly systems that are strongly linked to the characteristics of the final product make this solution more difficult. Hence, the implementation of such business models represents a clear challenge for machine tool builders and manufacturing system suppliers from technological perspective. This work discusses preliminary results in the technological implications of such strategy. A real industrial case related to cross-sector multi-stage automated assembly systems is presented to highlight the technological challenges and potential research directions. [ABSTRACT FROM AUTHOR]

From both economic as well as environmental perspective energy demand is of strong and even more increasing relevance for manufacturing nowadays. But typically energy demand and manufacturing system performance (e.g. output, time demands, quality) are considered separately while actually being inherently connected in technical systems. Against this background, the paper presents a data-based approach to analyze and model those important aspects and interdependencies in an integrated manner. The developed theoretical framework and defined methodology enable deeper understanding of energy related impacts of operative influencing factors and eventually to derive fields of action for improvement. The approach was exemplary applied in an industrial case study. Results underline the feasibility, applicability, and potential benefits of the approach. [ABSTRACT FROM AUTHOR]

The Circular Economy is struggling to establish itself in manufacturing systems, with the latest Circular Gap report showing that circularity is declining as the environmental situation worsens. Against this backdrop, several researchers have suggested that implementing emerging digital technologies of Industry 4.0 could be a key resource in this transition towards greater circularity in production. As circular manufacturing systems imply a relatively high complexity of flows, emerging technologies could be particularly useful in this context. After a brief review of the literature on material, information and human flows, a coupling with digital technologies is proposed to address the challenges of flow management in the circular economy. A critical analysis of the research on the use of digital technologies in the management of flows in circular manufacturing systems is then carried out. Finally, the perspectives for research in this area are presented, emphasising the need for multidisciplinary research and distinguishing the areas of research that can be commissioned to fill these research gaps. As part of a multidisciplinary project combining engineering, humanities and social sciences, this research prioritises an approach that equally integrates the three pillars of sustainable development (financial and technical, environmental and social). [ABSTRACT FROM AUTHOR]

A manufacturing system can simultaneously exploit more strategies from the Circular Economy paradigm, such as reduction, reuse, repair, refurbishment, and remanufacturing. These strategies involve finished products, raw materials, and components, and each adopted strategy can unpredictably impact the system performance along one or more dimensions (technical, economic, and environmental) at both the planning and the life cycle levels. This paper deepens the simultaneous measurement of the multi-dimensional and multi-level (strategic and tactical planning and life cycle design) performance of the manufacturing system in the presence of circular economy strategies. The Multi-layer Enterprise Input-Output (MEIO) formalisation method is extended to integrate Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) approaches to perform scenario analyses. The proposed approach is applied to a numerical example to show the impacts of adopting new processes for circular economy strategies in a brownfield context. The considered scenarios combine different circular economic strategies applied to different levels of the Bill of Materials of a multi-product environment. The proposed approach can help practitioners and production managers evaluate adopting circular economy strategies. Moreover, the approach can support the development of methods for simultaneous multi-dimensional performance assessment of manufacturing systems at the operational level. [ABSTRACT FROM AUTHOR]

Cyber-physical systems, cloud computing, the Internet of Things, and big data play significant roles in shaping digital and automated landscape manufacturing. However, to fully realize the potential of these technologies and achieve tangible benefits, such as reduced manufacturing lead times, improved product quality, and enhanced organizational performance, new decision support models need development. Game theory offers a promising approach to address multi-objective problems and streamline decision-making processes, thereby reducing computational time. This paper aims to provide a comprehensive and up-to-date systematic review of the literature on the application of game theory models in various areas of digital manufacturing, including production and capacity planning, scheduling, sustainable production systems, and cloud manufacturing. This review identifies key research themes that have been explored and examines the main research gaps that exist within these domains. Furthermore, this paper outlines potential future research directions to inspire both researchers and practitioners to further explore and develop game theory models that can effectively support the digital transformation of manufacturing systems. [ABSTRACT FROM AUTHOR]

This study explores the impact of augmented reality (AR) on worker performance in manufacturing contexts through an analysis of case studies extant in the literature. Two specific analyses were conducted to assess the impacts of AR technologies on worker performance in terms of objective and subjective metrics, and in terms of their age, experience with the task and experience with the AR device. Regarding objective metrics, the results showed that the task completion time was reduced for some AR devices (projectors, monitors, tablets, smartphones), whereas the use of the head-mounted display (HMD) increased task-completion time; moreover, the error rate was reduced with any AR device compared with traditional methods. Regarding subjective metrics, the analysis underlined that operator perceived a lower workload with the HMD or the monitor compared with traditional methods. The age of operators did not influence performance, while the operators' experience allowed for the improvement of human performance. [ABSTRACT FROM AUTHOR]

Abstract The chaotic identification and resolution, inadequate data interoperability, and inefficient management of resources in the high‐voltage coil production workshop limited the effectiveness of its management, and posed significant challenges. To address this issue, the authors establish a comprehensive interconnected digital workshop for high‐voltage coil manufacturing based on Industrial Internet Identification and Resolution as well as the 5G technology. A comprehensive framework model is developed for the high‐voltage coil workshop, along with a formal modelling and tagging approach for objects within the high‐voltage coil workshop. In addition, a management shell modelling method for the complete set of resources in the high‐voltage coil workshop is explored. An analytical identification and interoperability mechanism for the full resource of the high‐voltage coil workshop is introduced. Furthermore, a trusted shared space is developed for the complete resource data of the high‐voltage coil workshop. Finally, a field validation is conducted within a specific high‐voltage coil production workshop. The obtained results demonstrate that the proposed methods and models facilitate the unified access, mutual integration, and efficient management of the entire resources within the high‐voltage coil workshop. These achievements serve as a crucial reference for the implementation and advancement of interconnected manufacturing workshops.

With the advent of the socio-technical manufacturing paradigm, the way in which rescheduling decisions are taken at the shop floor has radically changed in order to guarantee highly efficient production under increasingly dynamic conditions. To cope with uncertain production environments, a drastic increase in the type and degree of automation used at the shop floor for handling unforeseen events and unplanned disturbances is required. In this work, the on-line rescheduling task is modelled as a closed-loop control problem in which an artificial autonomous agent implements a control policy generated off-line using a schedule simulator to learn schedule repair policies directly from high-dimensional sensory inputs. The rescheduling control policy is stored in a deep neural network, which is used to select repair actions in order to achieve a small set of repaired goal states. The rescheduling agent is trained using Proximal Policy Optimisation based on a wide variety of simulated transitions between schedule states using colour-rich Gantt chart images and negligible prior knowledge as inputs. An industrial example is discussed to highlight that the proposed approach enables end-to-end deep learning of successful rescheduling policies to encode task-specific control knowledge that can be understood by human experts. [ABSTRACT FROM AUTHOR]

AbstractProfile monitoring is a widely used tool in quality control. The rapid development of sensor technology has created unprecedented opportunities for multi-channel profile data collection, which motivates the modeling and transfer learning for multi-profile data. The Multi-output Gaussian Process (MGP) is often used for multi-profile data, due to its flexible modeling capability and elegant mathematical properties. However, two practical concerns limit the broader application of MGP for transfer learning and monitoring of multi-profile data: high computational complexity and data incompleteness. In this article, we propose a Variational Inference (VI)-based MGP framework to facilitate transfer learning and profile monitoring using incomplete profile data. The proposed framework features a specially designed convolutional structure for constructing an explicit covariance relationship between the inducing variables in VI and the MGP in multi-profile data. This structure inspires a comprehensive solution to both computational complexity and data incompleteness in modeling multi-profile data, which facilities the transfer learning for profile monitoring. Various numerical studies and one real case study are conducted to demonstrate and compare the transfer learning and monitoring performance of the proposed method. The results show the method can achieve superior monitoring performance while maintain a very low level of computational complexity. [ABSTRACT FROM AUTHOR]

The integrated product life cycle encompasses key stages in product development, production, and maintenance. Effective management of these stages is vital for operational efficiency and competitiveness. However, the impact of design, product configuration, and operational changes on manufacturing performance requires further investigation. This systematic literature review examines the interplay between the manufacturing systems' life cycle, design, configuration, and operations. Literature emphasizes the integration of software and digital tools to achieve operational excellence and competitiveness. The findings offer valuable insights for future research and aid decision-making for practitioners seeking to enhance manufacturing processes through effective integration of product, manufacturing, and digital technologies. [ABSTRACT FROM AUTHOR]

The complexity in the collaboration between humans and robots in smart manufacturing remains a significant challenge. This paper introduces an LLM-based manufacturing execution system enhancing Human-Robot Collaboration (HRC) in smart manufacturing. By leveraging Large Language Models (LLMs), the system provides a natural language interface for operators, integrates with Digital Twins for real-time data, and employs behavior-based control for robots. This integration facilitates intuitive interactions and rapid system programming, addressing communication complexities in HRC. The effectiveness of this approach is validated through two HRC assembly case studies, demonstrating significant improvements in collaboration and efficiency. [ABSTRACT FROM AUTHOR]

The reopening of business systems caused by the effects of the pandemic has forced decision-makers to look for quick and efficient mechanisms to achieve adequate levels of production and sales. For this reason, it is necessary to promote improvements from the processes and operations that allow agile and less complex systems.The objective of this research is to propose a new entropic metric for measuring complexity in manufacturing systems, providing its validation at each stage for a better procedural understanding. Methodologically, a literature review was carried out, followed by a detailed proposal of the new metric and finally a case study comparing it with classical and extended metrics. The results allow to solve the shortcomings found in these methods and show a better performance in the measurement. [ABSTRACT FROM AUTHOR]

Heterogeneous systems of limited capacity have general applications in manufacturing, but also in logistic or service systems due to the differences in server or workstation performance or work assignment; this is in close relationship with system flexibility, where saturation and blocking are ordinary situations of systems with high demand and limited capacity, and thus, accurate loss quantification is essential for performance evaluation. Multi-class systems of limited capacity have been studied much less than parallel homogeneous systems (Erlang models). In this context, accurate models for parallel heterogeneous ordered-entry systems were developed: without any prior queue, i.e., M/Mi/c/c, and with a k-capacity queue, i.e., M/Mi/c/c + k. These new matrix models gave an exact state formulation, and their accuracy was verified using discrete event simulation and comparison with literature results. Also, the effect of the queue capacity was studied in relationship to the pattern of service rates. Next, the heterogeneous recirculating system model was also developed with good approximation results. Finally, the proposed models were applied to evaluate systems with non-exponential service times using a new hybrid methodology by combining the Markovian model and the Monte Carlo method (MCM) for normal or lognormal service times, which also yielded useful good approximations to the simulated system. [ABSTRACT FROM AUTHOR]