Search

Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies. [ABSTRACT FROM AUTHOR]

Current studies on scheduling of machines and transporters assume that either a single transporter or an infinite number of homogeneous transporters such as AGVs or mobile robots are available to transport semi-finished jobs, which seems very restrictive in practice. This paper addresses this gap by studying a job-shop scheduling problem that incorporates a limited number of heterogeneous transporters, where the objective is to minimize the makespan. The problem is modelled using mixed-integer linear programming and constraint programming. Different structure-based branch-and-bound algorithms with two lower-bounding strategies are also developed. A comprehensive numerical study evaluates the proposed models and algorithms. The research demonstrates that the adjustment of the proposed MILP model outperforms the existing formulation when applied to the homogeneous case. The study also uncovers interesting practical implications, including the analysis of the impact of different transporter types in the system. It shows that utilizing a fleet of heterogeneous transporters can improve the overall performance of the job shop compared to a relevant homogeneous case. The importance of the study is emphasized by highlighting the negative consequences of disregarding transporters' differences during the scheduling phase. [ABSTRACT FROM AUTHOR]

Reconfigurable manufacturing systems (RMS) intend to bridge the gap between dedicated and flexible manufacturing systems. If the literature is mainly focused on the design step and tactical planning of such systems, few research projects have addressed scheduling at the operational level. While setup times may occur in flexible manufacturing systems, reconfiguration times considered in RMS may affect several resources at once, and hence require specific modelling and solving approaches to be considered. This paper first formalises the problem at hand through integer linear programming. An iterative search method is then provided to obtain solutions to larger-scale instances. Results obtained on generated instances show that managing even few possible configurations can yield significant improvements in solutions' quality. Meanwhile, the extended search space implied by the increase in available configurations hinders the convergence to a good solution in a reasonable computation time, which suggests further investigations. [ABSTRACT FROM AUTHOR]

In contemporary manufacturing and supply chain environments, there are various challenges that companies have to face. Many companies are facing challenges to respond quickly to customers' requirements and to provide customised products cost effectively. Mass customisation (MC) can help companies to provide customised products and services quickly at a low price. Integrated decision-making can obtain performance improvement in many situations. This paper investigates the problem of scheduling integration of flexible manufacturing systems (FMSs) for mass customisation. A mathematical model is formulated for the problem. A set-based dynamic algorithm is developed. Numerical studies are made on the proposed algorithm by simulation and by statistical analysis. Conclusions, managerial implications, and future research suggestions are provided. [ABSTRACT FROM AUTHOR]

This paper delves into the impacts of an ongoing global crisis on the resilience of supply chains. Furthermore, it proposes measures to address and mitigate the disruptions caused by the prevailing uncertainties. For example, while the economy has started to recover after the pandemic and demand has increased, companies have not fully returned to their pre-pandemic levels. To enhance their supply chain resilience and effectively manage disruptions, one viable strategy is the implementation of flexible/hybrid manufacturing systems. This research is motivated by the specific requirements of Vestel Electronics, a household appliances company, which seeks a flexible/hybrid manufacturing production setup involving dedicated machinery to meet regular demand and the utilisation of flexible manufacturing system (FMS) to handle surges in demand. We employ a scenario-based approach to model demand uncertainty, enabling the company to make immediate and adaptive decisions that take advantage of the cost-effectiveness of standard production and the responsiveness of FMS. To solve the problem, we propose a heuristic algorithm based on column generation. The numerical results demonstrate that our optimisation model provides solutions with an average optimality gap of less than 6% while also reducing the average cost of standard production schemes without FMS by over 12%. [ABSTRACT FROM AUTHOR]

The Special Issue on Scheduling Theory and Algorithms for Sustainable Manufacturing was initiated at the MIM 2022 conference in Nantes, France. The issue focuses on how scheduling theory and algorithms can address complex optimization problems in manufacturing from sustainability perspectives. Various papers in the issue explore topics such as reinforcement learning in production scheduling, remanufacturing through machine learning, and sustainable inventory management in multi-storage supply chains. The articles aim to provide efficient solutions for planning and scheduling issues in digital, sustainable, and human-centric manufacturing and logistics. [Extracted from the article]

In the context of Industry 4.0 and intelligent manufacturing, AGVs are widely used in flexible job shop resource transportation, which sharply increases the uncertainty and complexity of the scheduling process. For this reason, an improved double Deep Q Network (DDQN) real-time scheduling method is proposed for the Flexible Job Shop Scheduling Problem with Automated Guided Vehicle (FJSP-AGV) to minimize the makespan. Firstly, the optimization model of the FJSP-AGV is established, and the corresponding constraints and the objective function are defined. Then, the FJSP-AGV is converted into a Markov Decision Process (MDP), in which the state space, action space, and reward function are defined in detail. Next, an improved DDQN is proposed to generate the optimal scheduling policy considering AGV. Finally, the computational experiments are conducted based on data from public benchmarks and the real-world flexible job shop, and the results demonstrate the accuracy and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

3D printing offers a cost‐effective solution for rapidly prototyping and customizing composite products. The integration of multi‐axis robotic systems with the printing process significantly enhances motion control, design flexibility, and manufacturing scalability. In this study, a robot‐assisted manufacturing platform and the associated digital workflow for the 3D printing of UV‐curable continuous fiber‐reinforced polymer composites (CFRPCs) is introduced. Specifically, a transferable protocol is established for robotic 3D printing of CFRPCs, which involves coordinate calculation, trajectory generation, and validation checks. This protocol enables the printing of composite samples or large‐scale structures on both planar substrates and curved 3D substrates. Additionally, composite printing on substrates with unknown profiles using laser‐based 3D scanning is demonstrated. Overall, the developed printing method and workflow are applicable to a broader range of feedstock materials and robotic manipulators, which makes this study a valuable resource for future developments in 3D‐printed CFRPCs. [ABSTRACT FROM AUTHOR]

The editorial discusses the significance of advanced manufacturing and precision machining in driving industrial transformation and enhancing international competitiveness. It highlights the integration of interdisciplinary technologies to improve manufacturing precision and efficiency, particularly in high-tech industries like aerospace and medical devices. The article also addresses challenges such as balancing precision with efficiency and transitioning towards greener manufacturing practices. Overall, advancements in these fields are crucial for meeting complex industrial demands and driving technological progress towards sustainable manufacturing practices. [Extracted from the article]

This study addresses the Distributed Flexible Job-Shop Scheduling Problem (DFJSP) while considering the delivery of customer orders and transportation time. To tackle the problem, a mathematical model is presented, and a novel variant of the Genetic Algorithm (GA), called Time-Travel GA (TTGA), is developed. The optimization objectives include minimizing the total orders’ delivery time to the related customers, reducing transportation and production costs, reducing manufacturing pollution, and maximizing the quality of completed orders. The performance of TTGA is tested by implementing it in a real-life manufacturing setting. Additionally, TTGA results are compared with those of four other algorithms over a set of test problems. Furthermore, the solutions obtained by TTGA are compared with the optimal solutions obtained by a commercial solver for small-size problems. The results of these comparisons collectively demonstrate the promising performance of TTGA in addressing the DFJSP. [ABSTRACT FROM AUTHOR]

Employing cognitive robots, capable of throwing and catching, is a strategy aimed at expediting the logistics process within Industry 4.0's smart manufacturing plants, specifically for the transportation of small-sized manufacturing parts. Since the flight of mechanically thrown objects is inherently unpredictable, it is crucial for the catching robot to observe the initial trajectory with utmost precision and intelligently forecast the final catching position to ensure accurate real-time grasping. This study utilizes multi-camera tracking to monitor mechanically thrown objects. It involves the creation of a 3D simulation that facilitates controlled mechanical throwing of objects within the internal logistics environment of Industry 4.0. The developed simulation empowers users to define the attributes of the thrown object and capture its trajectory using a simulated pinhole camera, which can be positioned at any desired location and orientation within the in-plant logistics environment of flexible manufacturing systems. The simulation facilitated ample experimentation to be conducted for determining the optimal camera positions for accurately observing the 3D interception positions of a flying object based on its apparent size on the camera's sensor plane. Subsequently, a variety of calibrated multi-camera setups were experimented while placing cameras at identified optimal positions. Based on the obtained results, the most effective multi-camera configuration setup is derived. Finally, a training dataset is prepared for 3000 simulated throwing experiments where the initial part of the trajectory consists of observed interception positions, through derived best multi-camera setup, and the final part consists of actual positions. The encoder–decoder Bi-LSTM deep neural network is proposed and trained on this dataset. The trained model outperformed the current state-of-the-art by accurately predicting the final 3D catching point, achieving a mean average error of 5 mm and a root-mean-square error of 7 mm in 200 real-world test experiments. [ABSTRACT FROM AUTHOR]

Featured Application: The findings of this study contribute to the development of more versatile robotic systems capable of efficiently performing complex dynamic tasks in variable environments. This work enhances the potential for integration of such systems into flexible manufacturing processes, enabling greater adaptability and responsiveness in dynamic operational scenarios. This paper presents an experimental investigation into the implementation of free-motion tasks on a serial metamorphic manipulator (SMM). Utilizing a previously established task-based optimization methodology, the dynamic performance of the SMM is evaluated through a combination of theoretical performance metrics and experimental data. The study aims to validate the SMM's ability to achieve optimized performance through structural reconfiguration. Theoretical models are compared against real-world free-motion task data, demonstrating strong correlations between analytical calculations and experimental outcomes. The discussion focuses on three key areas: the efficiency of joint controllers, end-effector acceleration capabilities, and joint controller performance. Results indicate that an optimized anatomy can achieve more than 40% reduction in produced torques during task execution and a 35% improvement in the torque-to-velocity ratio. While the simple controller implemented in the robot prototype exhibits adequate performance, notable limitations are observed in task segments with lower dynamic performance, particularly in terms of positional accuracy and energy efficiency. During XY-plane task execution, the Z-axis position error deviates by 1 to 2 cm in areas of lower dynamic performance. These findings provide key insights and establish a robust foundation for advancing SMM capabilities in practical applications, with future work focusing on addressing the identified limitations. [ABSTRACT FROM AUTHOR]

Manufacturing systems are undergoing systematic change facing the trade-off between the customer's needs and the economic and ecological pressure. Especially assembly systems must be more flexible due to many product generations or unpredictable material and demand fluctuations. As a solution line-less mobile assembly systems implement flexible job routes through movable multi-purpose resources and flexible transportation systems. Moreover, a completely reactive rearrangeable layout with mobile resources enables reconfigurations without interrupting production. A scheduling that can handle the complexity of dynamic events is necessary to plan job routes and control transportation in such an assembly system. Conventional approaches for this control task require exponentially rising computational capacities with increasing problem sizes. Therefore, the contribution of this work is an algorithm to dynamically solve the integrated problem of layout optimization and scheduling in line-less mobile assembly systems. The proposed multi agent deep reinforcement learning algorithm uses proximal policy optimization and consists of a decoder and encoder, allowing for various-sized system state descriptions. A simulation study shows that the proposed algorithm performs better in 78% of the scenarios compared to a random agent regarding the makespan optimization objective. This allows for adaptive optimization of line-less mobile assembly systems that can face global challenges. [ABSTRACT FROM AUTHOR]

Automated guided vehicle (AGV) scheduling has become a hot topic in recent years as manufacturing systems become flexible and intelligent. However, little research regards dynamic AGV scheduling considering energy consumption, particularly battery replacement. This paper proposes a novel method that employs deep reinforcement learning to address the dynamic scheduling of energy-efficient AGVs with battery replacement in production logistics systems. The bi-objective joint optimization problem of AGV scheduling and battery replacement management is modeled as a Markov Decision Process, which supports data-driven decision-making. Then, this paper constructs a deep reinforcement learning-based optimization architecture and develops a novel dueling deep double Q network algorithm to maximize the long-term rewards for optimizing material handling's tardiness and energy consumption. Numerical experiments and a case study demonstrate that the proposed algorithm is more efficient and cleaner than state-of-the-art methods. The proposed method can significantly improve customer satisfaction and reduce production costs within flexible manufacturing processes, particularly in Industry 4.0. [ABSTRACT FROM AUTHOR]

All-solid-state batteries (ASSBs) with lithium metal anodes represent a potential future battery technology due to their increased energy density and operational safety. The modified materials and cell design compared to the currently predominating lithium-ion batteries (LIBs) entail significant changes in manufacturing, rendering existing industrial battery production lines incompatible with lithium-metal-based ASSB fabrication. This study introduces a research-grade, semi-automated prototype production system for assembling lithium-metal-based ASSBs with various solid electrolyte types, detailing the adaption from LIB manufacturing processes to those suitable for ASSBs. Key focus areas include the transition in lamination, cutting, and stacking processes, with special emphasis on the integration of equipment within controlled atmospheric environments. The installation of a calender within a glovebox system for thinning and laminating ASSB components is discussed. Additionally, the implementation of a laser system for precise shaping and surface treatment of electrodes and electrolyte materials is presented. The development of a robotic handling system for cell stacking is explored, facilitating the use of different gripping principles for handling delicate lithium and sheet-type solid electrolyte layers. This research supports stakeholders from research and industry in identifying essential equipment modifications for lithium-metal-based ASSB production, providing a foundation for next-generation battery assembly systems. [ABSTRACT FROM AUTHOR]

Industry 4.0 aims to establish highly flexible production, enabling effective and efficient mass customisation of products. Modelling techniques and simulation of production processes are among the core techniques of the manufacturing industry that facilitate flexibility and automation of a shop floor in the era of Industry 4.0. In this paper, we present an approach to support production process modelling and process model management. The approach is based on Model-Driven (MD) principles and comprises a Domain-Specific Modelling Language (DSML) named Multi-Level Production Process Modelling Language (MultiProLan). MultiProLan uses a set of concepts to specify production process models suitable for automatic instruction generation and execution of the instructions in a simulation or on a shop floor. By using MultiProLan, process designers may create process models independent of the specific production system. Such process models can either be automatically enriched by matching and scheduling algorithms or manually enriched by a process designer via MultiProLan's modelling tool. In this paper, we also present an application of our approach in the assembly industry to showcase its dynamic resource management, generation of production documentation, error handling and process monitoring. [ABSTRACT FROM AUTHOR]

Circular RNA (circRNA) exhibits a higher stability and intracellular half-life than linear RNA and has better potential in the fields of RNA vaccines and RNAi drugs. The current strategies for circRNA preparation have low efficiency, high costs, and high complexity, which significantly limits their applications. In this paper, we propose a one-step synthesis of circRNA based on E. coli. The four RNA sequence lengths of 1700, 1400, 500, and 64 nt were connected to group II intron elements from the surface protein region of Clostridium tetani and then inserted downstream of the T7 promoter in the pET28a plasmid to assist in cyclization. Then, circRNA was produced in HT115, where the yields of pET28-1700, pET28-1400, pET28-500, and pET28-64 were improved to 820, 783, 691, and 460 ng/1 mL, respectively. Consequently, this system could achieve the mass production of circRNA using only a simple E. coli culture and inducible expression. Meanwhile, the overexpressed circRNA and small circular interference RNA (sciRNA) maintained their biological functions in the protein translation and RNAi. Therefore, this simple and efficient one-step synthesis system can be applied to the functional study and preparation of circRNA in the future. [ABSTRACT FROM AUTHOR]

In Industry 4.0, Automated Guided Vehicles (AGVs) enhance material handling efficiency and cost reduction. However, research on multi-objective scheduling of jobs, tools, automated storage, and AGVs in Flexible Manufacturing Systems (FMS) is limited. This study introduces the Novel Genetic and Adaptive Artificial Bee Colony Algorithm (NG-AABCA) to minimize the makespan, total tardiness, and penalty costs. NG-AABCA integrates cognitive (e1) and social (e2) learning factors, often overlooked, to achieve optimal solutions by leveraging external sources like the global optimal solution. This approach expedites convergence and avoids local optima by adjusting parameters iteratively. The Genetic Algorithm component employs elitism and Random-Restart Hill-Climbing to balance solution quality and diversity. Compared to other algorithms, NG-AABCA reduces makespan by 5.3% and tardiness by 8.7%, promising increased productivity and efficient resource use. This robust method aims to transform manufacturing optimization in Industry 4.0, addressing complex scheduling challenges in FMSs effectively. [ABSTRACT FROM AUTHOR]

The research presented in this paper aligns with the advancement of Industry 4.0 by integrating intelligent machine tools and industrial robots within Flexible Manufacturing Systems (FMS). Primarily, a development approach for Digital Twin (DT) is presented, beginning from the design, sizing, and configuration stages of the system and extending through its implementation, commissioning, operation, and simulation-based optimization. The digitization of current industrial processes entails the development of applications based on modern technologies, utilizing state-of-the-art tools and software. The general objective was to create a digital replica of a process to propose optimization solutions through simulation and subsequently achieve virtual commissioning. The practical nature of the research is reflected in the design and implementation of a Digital Twin for a real physical system processing a family of cylindrical parts within an existing experimental FMS. A digital model of the system was created by defining each individual device and piece of equipment from the physical system, so the virtual model operates just like the real one. By implementing the Digital Twin, both time-based and event-based simulations were performed. Through the execution of multiple scenarios, it was possible to identify system errors and collisions, and propose optimization solutions by implementing complex, collaborative-robot equipment where multiple interactions occur simultaneously. [ABSTRACT FROM AUTHOR]

Production rescheduling involves re-optimizing production schedules in response to disruptions that render the initial schedule inefficient or unfeasible. This process requires simultaneous consideration of multiple objectives to develop new schedules that are both efficient and stable. However, existing review papers have paid limited attention to the multi-objective optimization techniques employed in this context. To address this gap, this paper presents a systematic literature review on multi-objective production rescheduling, examining diverse shop-floor environments. Adhering to the PRISMA guidelines, a total of 291 papers were identified. From this pool, studies meeting the inclusion criteria were selected and analyzed to provide a comprehensive overview of the problems tackled, dynamic events managed, objectives considered, and optimization approaches discussed in the literature. This review highlights the primary multi-objective optimization methods used in relation to rescheduling strategies and the dynamic disruptive events studied. Findings reveal a growing interest in this research area, with "a priori" and "a posteriori" optimization methods being the most commonly implemented and a notable rise in the use of the latter. Hybridized algorithms have shown superior performance compared to standalone algorithms by leveraging combined strengths and mitigating individual weaknesses. Additionally, "interactive" and "Pareto pruning" methods, as well as the consideration of human factors in flexible production systems, remain under-explored. [ABSTRACT FROM AUTHOR]

Because disruption happens unpredictably and generates serious impact in supply chain and production environments in the real world, it is important to develop approaches to handle disruption. This paper investigates disruption handling in part input sequencing of flexible manufacturing systems (FMSs). An algorithm is proposed for FMS part input sequencing to handle machine breakage. Evaluation is performed for the proposed algorithm by simulation experiments and result analyses. Finally, conclusions are summarized with managerial implications discussed and further research works suggested. [ABSTRACT FROM AUTHOR]

In recent years, manufacturers have increasingly applied automation techniques to enhance efficiency and remain competitive. Material handling is an essential activity in any production process. Its effectiveness has a significant impact on production costs. Automated guided vehicle (AGV) systems have become a strategic tool for factories and automated warehouses. They can reduce production costs and improve delivery times in a competitive business scenario. One of the main problems encountered in managing AGVs is the dispatching decision. The present paper proposes a vehicle dispatching system based on a multi-attribute cascaded fuzzy system with a state-space-based Petri net model to predict the future states of the factory. This work aims to reduce the production system's makespan and/or tardiness values. Additionally, the paper presents one simulated factory scenario to evaluate the proposed fuzzy system against five other dispatching methods. The statistical validation results of the simulations show a 97% confidence level in the hypotheses of this work. [ABSTRACT FROM AUTHOR]

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS. [ABSTRACT FROM AUTHOR]

In manufacturing and service systems, the negative effects of variability propagation are usually addressed through approximation models and other tools, such as simulation and optimization algorithms. This paper investigates the conditions in which these approaches are ineffective, and considering only the mean and variance of the processing time distribution is misleading. A scenario analysis deepens the impacts of considering the entire processing time distribution (beyond its mean and variance) on the inter-departure times in balanced and unpaced lines modeled through Discrete Event Simulation. The results correlate the impacts on variability propagation of approximating the processing time distribution with system characteristics such as utilization levels, line sizes, and inter-arrival and processing time variability. Production planning approaches can benefit from these results to reduce variability propagation, particularly in flexible and reconfigurable manufacturing systems, largely adopted in Industry 4.0, that can highly influence processing time distributions by varying product mix and line configuration. A balanced and unpaced line, with a single inter-arrival time distribution, and two processing time distributions, alpha and beta, rispectively, which have the same Squared Coefficient of Variation and different shape that lead to different variability propagation in the inter-departure time distribution at the end of the line. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

AGV-based intra-company transport systems are indispensable in the manufacturing industry of Industry 4.0. Designing the systems involves determining AGV movement paths that are predefined dynamically or adjusted based on real-time events. This study focuses on the simulation and optimization of an intelligent transport system. The aim is to create a system model with freely moving AGVs controlled based on the requirements of production facilities. The simulation model was designed in the Tecnomatix Plant Simulation environment. A fictional case study with a flexible manufacturing system was used. Specific methods have been developed for AGV operation, control, and dynamic product handling. The initial simulation model served as the basis for optimization. Model optimization, performed using a genetic algorithm, aimed to maximize production volume while minimizing the number of AGVs. Simulation results showed that AGV movements were dynamically adjusted based on real-time machine requests, and the optimal configuration of AGVs achieved a production volume that was significantly higher than the initial setup. This study demonstrates a new approach to modeling AGV traffic systems emphasizing real-time dynamic adjustments of AGV paths. The findings contribute to integrating intelligent transport systems into production processes, and this study provides valuable insights for future investigation in this area. [ABSTRACT FROM AUTHOR]

Automatic Guided Vehicles (AGVs) are widely used in flexible manufacturing systems for material handling inside the factory. Traffic management strategies, required to guarantee a conflict-free operation of the overall fleet, discretize the workspace of the AGVs and use the resulting graph model for route planning and execution. In zone control approaches, AGVs move from node to node on a permit basis, with limitations on the allowed number of AGVs at a time in each area of the graph to prevent and/or resolve deadlocks and conflicts. Hence, for an optimal implementation of traffic controllers in real manufacturing systems, it is essential to understand how the layout discretization influences the performance of the AGV network. This paper analyzes its effect in grid-like shaped workspaces by using a representative zone control algorithm and a recently developed improvement of it. Realistic numerical experiments on different layouts reveal that denser discretizations do not yield faster executions or increase in throughput, while lower control periods in the permit system entail significant performance uplifts. [ABSTRACT FROM AUTHOR]

Flexible manufacturing is an important path for the rapid development of the manufacturing industry. However, there is no specific evaluation standard for the flexibility level of clothing manufacturing, making it difficult for clothing enterprises to estimate the progress of their own flexible manufacturing and the parts that need improvement. In response to this situation, taking S clothing enterprise as an example, the production line balance optimization was taken as the starting point, a three-dimensional evaluation model for flexible manufacturing of clothing was constructed from three aspects; production flexibility, management flexibility, and product flexibility, the flexibility level of S clothing enterprise manufacturing was systematically and comprehensively evaluated. The results indicate that clothing enterprise has a high level of flexible manufacturing and good development prospects. It can continue to improve its existing production mode and further enhance its clothing flexible manufacturing level. The evaluation index system for flexible manufacturing of clothing in this article provides a good foundation and a systematic model for the future flexible manufacturing process and industry standardization of small and medium-sized clothing enterprises, based on the successful practice of S clothing enterprise. [ABSTRACT FROM AUTHOR]

In the present study, we employed multiple decision tree algorithms to categorize cases and reflect the most efficient policies constructed by a reinforcement learning algorithm. These approaches treated a complex production, maintenance, and quality control optimization problem within a degrading manufacturing and remanufacturing system. The decision trees' nodes represent the independent variables, while the trees' leaves represent the set of function values. The reinforcement learning method revealed all optimization parameters and best policies, which were employed as the training sample for the tree algorithms. After constructing every decision tree, each resulting decision rule was used to solve the optimization problem, and its performance was assessed. Additionally, we performed a sensitivity analysis to determine if the pruning level impacts the objective function value and, generally, the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The nature of complementary products is the dependency of one product on the other for utility. Further, the complementary products with deteriorated nature create a challenging environment for a suitable strategy. How does an industry deal with complementary deteriorated products at the retailer's house? The present study deals with two complementary deteriorating products in a two-echelon supply chain management. The deterioration of complementary products increases with time, and the deterioration rate is taken as the function of time. Complementary deteriorating products are manufactured by two manufacturers and sold to a common retailer. Manufacturers use a flexible production system to produce products and a single-setup-multiple-delivery policy to deliver those products. Under the flexible production system, emissions from setup, holding products, disposal, and transportation of products are considered. A carbon tax policy is used to reduce emissions. This study aims to optimize the total profit of the supply chain by finding a suitable sales strategy. The total profit is maximized by acquiring the optimal values of the retail price, cycle time of the retailer, production rate, and number of shipments. The numerical result shows that the profit becomes maximum when complementary products are less dependent on each other. The result indicates that adapting the flexible production system increases the total profit by 1.86% and decreases total emissions by 5.36% than a traditional production system. [ABSTRACT FROM AUTHOR]

Flexible process planning (FPP) involves selecting and sequencing the requisite operations according to technological requirements, and meanwhile allocating a right machine, a right tool and a right access direction to each selected operation by a given criterion. In this article, the FPP problem is exactly and concisely formulated as linear integer programming models based on the topology of the AND/OR-network under two criteria: production cost minimisation and completion time minimisation. Distinctively, more flexible manufacturing elements and process plan evaluation criteria are considered; more complicated tool and access direction changeover identifications are linearly expressed without the big-M parameter. Compared with the latest mathematical programming models for process planning, the proposed models have lower complexity and better performance. The results from numerous comparative experiments indicate that (i) the number of decision variables of the proposed models reduces approximately by 68% and the number of constraints of the proposed models dramatically reduces by 99%; (ii) within the same running time, the proposed models can exactly solve more benchmark cases than the latest models; and (iii) the solutions obtained by the proposed models are also better than the best ones founded by some state-of-the-art meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

At the stage of production preparation, there is an urgent need for an automated system that would timely detect signs of threats to information security and the emergence of unreliable data. To solve this problem, an intelligent module capable of detecting such threats and unreliable and/ or anomalous data has been designed. The proposed intelligent module is the state-of-art, original, and effective toolkit. It can be recommended for practical use as part of the well-known information and computer system for automated modeling of the system of automatic orientation of production objects at the stage of technological preparation of machine and instrument-building production. Its application makes it possible to increase information security and reliability of important production data at the stage of technological preparation of production, in particular, when modeling systems for automatic orientation of production objects. In addition, the use of the proposed intelligent module makes it possible to obtain a number of important social and economic effects. Some of these effects are manifested in the prevention or reduction of material, intellectual and time costs for saving and restoring information, etc. Automated analysis of important production data regarding their reliability and abnormality is carried out by machine learning methods using a specially designed advanced variational autoencoder based on classification algorithms and using wavelet transformation. The designed intelligent module for detecting signs of a threat to information security and the emergence of unreliable and/or anomalous data works in real time with a high accuracy of 97.53 %. It meets the requirements of modern production. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive evaluation of a virtual reality-based testing station designed for flexible manufacturing systems. Given the intricate nature of flexible manufacturing systems and the demand for precision in learning, the integration of virtual reality emerges as a promising approach to enhance both student competence and engagement. By employing a combined assessment with the System Usability Scale and heuristic evaluation conducted by 36 students and 5 experts, respectively, the virtual reality-based testing station achieved an average usability score of 72.78, indicating good usability. Noteworthy heuristic challenges, particularly in the domains of 'Realistic Feedback' and 'Navigation and Orientation Support,' have been identified, providing valuable insights for potential refinements to the testing station. The outcomes of this study not only guide immediate improvements but also pave the way for future research endeavors aimed at elevating the learning outcomes in flexible manufacturing systems courses. [ABSTRACT FROM AUTHOR]

The Hayes-Wheelwright Product-Process Matrix (PPM) has historically been a pivotal tool in manufacturing, guiding firms to strategically align product volume and variety with the most optimal production process. Delving into its developmental journey, this research thoroughly examines the PPM's evolution through the industrial eras. From the outset of Industry 2.0, marked by the adoption of Flexible Manufacturing Systems prioritizing mass customization, to the inception of Industry 4.0, characterized by a seamless integration of digital and physical systems with a pronounced focus on tailored products, the matrix has consistently adapted. The main findings of this work underline the imperative to reconceptualize the PPM within the context of Industry 5.0. Building on this foundation, a new model is here proposed to represent the PPM matrix in the Industry 5.0 environment that incorporates creative and technological innovation and the concept of sustainability. This entails a fusion of cutting-edge technological developments, urgent sustainability considerations, and the unyielding deep human insights. The proposed matrix (PPM 5.0) maps each product to the most suitable production process and guides companies in selecting processes that align with sustainable objectives. Furthermore, it provides a flexible foundation designed to readily adapt to evolving market demands, technological advancements, and emerging trends in manufacturing, offering substantial benefits to practitioners and business owners in their strategic design decisions and technology investments. To achieve these insights, a qualitative methodology is employed, grounded in an exhaustive review of the extant literature, thereby ensuring a holistic and informed perspective on the future trajectory of industrial practices. [ABSTRACT FROM AUTHOR]

This article discusses an experimental study and mathematical modeling of face milling forces on high-strength, high-viscosity shipbuilding steel. The study focuses on preparing edges of holes in ship hull structures for welding, proposing the use of non-stationary technological complexes (NTC) with numerical control for machining complex surfaces. The article presents a mathematical model of the milling forces and discusses challenges and potential solutions for machining high-strength steel. The findings aim to contribute to the development of special equipment for processing hull structures under non-stationary conditions. [Extracted from the article]

Background: The article discusses the problem of modeling and simulation of the production process, as well as accompanying intralogistics processes in the automotive industry, which are characterized by advanced production standards and high product quality. The analyzed processes concern a Flexible Manufacturing System (FMS) for manufacturing a family of gear wheels and a distribution center that operates in a cross-docking regime. The goal of the research is to create a methodology of modeling and simulation of production and intralogistics processes with the help of a digital twin, which aims to integrate the design of production and logistics systems, considering factors such as manipulation and transportation of each product. Therefore, the article emphasizes the optimization of intralogistics processes. Methods: The proposed methodology is based on the Value Stream Mapping (VSM) method, which is used for identifying critical components of the production system being analyzed. The first step involves creating a VSM of the current state of the production system, which can be utilized to model key research areas, such as current planning, scheduling, manufacturing, and intralogistics. Analyzing the map and related models leads to the development of a future state map, used to create improved models of the system. Subsequently, the models are integrated into a digital twin with the use of the Flexsim program. Results: The results of simulation experiments confirm that the introduction of a FMS with AGV transport allows improvement in the intralogistics system, reduces the production cycle of a planned order package from two days to one day, and improves the distribution process with the use of pallets and short transportation batches, which enables 64 shipments during one shift. The experiments also confirm the stability of the model for different numbers of AGVs and a random inflow of orders. Conclusions: The proposed methodology of creating a digital twin makes it possible to evaluate the effectiveness of the proposed improvements and to verify production plans. The advantage of the applied simulations and analyses is the significant reduction in the amount of time spent searching for optimal solutions and storage capacities. [ABSTRACT FROM AUTHOR]

New developments in the area of the Industrial Internet‐of‐Things (IIoT) and Industry 4.0 offer huge potential for a more efficient and flexible industrial production, but are also accompanied by rising system complexity. Consequently, to deal with the increased system complexity, novel approaches, such as reference architectures, are emerging. However, most of these approaches are not yet mature and rather theoretical than ready‐to‐use. Thus, companies need to be provided with frameworks that actively support the transformation of their systems towards Industry 4.0. One of those frameworks has been introduced with Reference Architecture Model Industry 4.0 (RAMI 4.0), which counteracts the mentioned complexity and can be used for various use cases. However, as most of its concepts are too general to be applied directly to actual systems, the need for directly applicable reference architectures emerges. Therefore, this paper proposes a method to derive more detailed reference architectures based on RAMI 4.0 by making use of model‐based systems engineering (MBSE), which target single manufacturing domains rather than the whole industry. Therefore, relevant stakeholders are analyzed and different types of reference architectures targeting their concerns are identified. The resulting reference architectures should be ready‐to‐use for interested manufacturers and thus, enhance the acceptance of RAMI 4.0 as well as improve systems engineering in industrial manufacturing. Finally, the developed reference architecture is evaluated in a proof‐of‐concept case study of a flexible production system. [ABSTRACT FROM AUTHOR]

Automated material transfer between workstations is a key feature of flexible manufacturing systems. The aim of automation is to increase the output rate while reducing the manufacturing throughput. However, machine idle time contributes significantly to the overall throughput time and cannot be completely eliminated; it can only be minimized. Accurately locating the bottleneck and synchronizing it with other assembly line equipment can help reduce throughput. The process or activity with no idle or waiting time is known as the bottleneck in a production system. In this paper, we will analyze the bottleneck in Coca-Cola's production line at Lae and provide suggestions for reducing throughput. [ABSTRACT FROM AUTHOR]