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Scheduling of assembly manufacturing systems has received much attention in recent years, but the scheduling problem of deadlock-prone flexible assembly systems (FASs) is rarely studied. Based on the Petri net models of FASs, this article studies the scheduling problem of deadlock-prone FASs, and the heuristic beam search (HBS) algorithm is proposed to minimize the makespan. In HBS, the state space of the FAS is searched level by level, and two types of evaluation functions are proposed to ensure that only the most promising states are explored. To find a better solution in an acceptable time, the HBS algorithm is integrated with the iterative policy, and the iterative widen heuristic beam search (IWHBS) algorithm is obtained. Experimental and comparison results validate the effectiveness and superiority of the IWHBS algorithm over other heuristic search algorithms. [ABSTRACT FROM AUTHOR]

The growing relevance of digitalization in production requires the enhancement of human skills and competences in the field of Information and Communication Technology (ICT). Higher education has to cope with this need by providing the necessary ICT skills to future industrial engineers, so that they have a good understanding of the complexity of industries in the 21st century. This paper presents the conceptual development and testing of a Virtual Learning Factory Toolkit (VLFT) that integrates digital tools used in production management with engineering education. The digital tools integrated into the VLFT can help students to exploit enabling technologies such as simulation and virtual reality in their manufacturing studies and practical projects with industrial companies. Moreover, digital tools were tested by using a structured workflow that consists of different learning activities related to manufacturing system configuration. Students practised the digital tools with the help of use cases in the form of joint learning labs, after which the students' feedback was collected and analysed. [ABSTRACT FROM AUTHOR]

Manufacturing systems with perishable products appears in several industrial branches such as food production, chemical and radioactive material manufacturing, and pharmaceutical industry. Product perishability often results in the disposal of the fraction of products that do not meet the final specifications, after being kept too long within serviceable inventory. The disposal of perishables raises several environmental issues and its minimization together with reduction of other costs related to perishable inventories are most often studied within inventory management framework. The production control problem for the systems with perishable products, especially in case of failure-prone manufacturing facilities, is an important and challenging topic that was only sparsely addressed in the scientific literature. The main difficulty consists in resolving the trade-off between having an inventory level sufficient to fill the demand in case of machine failure, and potential deterioration of products kept too long within inventory. The paper proposes an approach to resolve this trade-off. First, a mathematical model describing the stochastic behavior of such systems is developed. Next, an analytical study of the system is performed, and finally, an optimal production policy that takes the product perishability into account is determined. This policy is shown to be a hedging point-type policy; thus the well-known result about the optimality of hedging point policy (in nonperishable case) is generalized to the class of perishable products with fixed shelf-life. It is shown that under conventional conditions (limited production capacity, Markovian failure/repair processes, constant demand rate), the optimal hedging level does not exceed the cumulative demand along the product shelf-life, and that leads to no disposal of products, known as a zero-waste policy. Numerical simulations allowed to perform the sensitivity analysis and a comparative study of the costs incurred in the systems with and without perishability. [ABSTRACT FROM AUTHOR]

This article addresses the scheduling problem of deadlock-prone flexible manufacturing systems subject to no-wait constraints for the first time, and develops a new scheduling algorithm based on the place-timed Petri net (PN) model and heuristic search. The considered problem can be solved by two procedures: 1) timetabling and 2) sequencing. The timetabling is to translate a given job sequence into a feasible schedule that satisfies the deadlock-free and no-wait constraints. A novel timetabling algorithm based on the controlled PN model is then proposed for solving it. The goal of sequencing is to find a job sequence so that the makespan of the corresponding schedule is the minimum. To this end, a hybrid heuristic search (HHS) is developed by combining the ${A}$ * algorithm and dynamic window. Two new heuristic functions are designed to guide the search, and the dynamic window is used to limit the searched space. The performance of HHS with different heuristic functions and deadlock controllers is evaluated by ten instances. The computational results demonstrate that through the proposed approach, optimal or suboptimal feasible schedules can be obtained within a reasonable time. [ABSTRACT FROM AUTHOR]

This paper measures the reliability of a seru production system, which has been proven to be more flexible, efficient, and responsive than the traditional assembly lines in production practice. Seru is the Japanese pronunciation for "cell", and it is a new type and flexible parallel low automated assembly mode originated from Japanese production practice. This is the first research that focuses on the evaluation of the reliability of seru production systems. We take into account reworking actions of some defective products, especially when these defective products have high values. In this seru production system, the capacity of each worker in the seru systems is regarded as stochastic according to the practical situation. The whole processes of each seru can be decomposed into a general process and a reworking process. In order to analyze the reliability of a seru production system, a model is constructed to describe these two process paths. A solution method is designed to obtain the seru production system reliability. Finally, some numerical examples are presented, and the reliability between seru production system and traditional assembly line is compared. We found that seru production systems are more reliable than assembly lines. [ABSTRACT FROM AUTHOR]

This work investigates a scheduling problem of deadlock-prone flexible manufacturing systems modeled by place-timed Petri nets. It proposes an anytime branch and bound (AB&B) algorithm for it to minimize system makespan based on the branch tree of a net model and a highly permissive deadlock controller. The proposed algorithm searches a sequence of transitions in the branch tree that evolves the model from the initial marking to the final one. In order to prune the branch tree and increase search speed, this work develops two pruning rules, a lower bound of makespan, and a novel branching strategy. Their usage ensures AB&B’s high search efficiency. Experimental results demonstrate that the proposed algorithm surpasses the state-of-the-art ones. Note to Practitioners—In practice, scheduling is one of the most important issues for production managers to address. Existing approaches to deadlock-prone flexible manufacturing system (FMS) scheduling have multiparameters. Their settings can impact the scheduling results greatly. Since their turning is a challenging task, it is difficult to find the best parameter settings. This article proposes an anytime branch and bound (AB&B) algorithm for minimizing the makespan of deadlock-prone FMSs. It has only one parameter, i.e., maximal CPU time, which needs no turning. When it is highly tight, AB&B can output a feasible and decent schedule. If it is larger and larger, AB&B can output a better and better schedule. Comparison studies show that AB&B outclasses existing ones significantly. It is suitable for real-time scheduling cases in which satisfied schedules must be offered in a very short time. [ABSTRACT FROM AUTHOR]

Featured Application: The application in this article describes creating a data structure of the working activities in the framework of a parametric model for production operators. The project system calculates the working utilisation through the data structure and describes the methodology for assessing various variants. The requirement to achieve effective solutions in the shortest possible time in the manufacturing environment is essential, and it can be solved only by effective production planning methods. The scientific problem is that traditional methods for creating and assessing the production plans are insufficient for the future and it is necessary to look for new alternatives. The planners in the framework of designing the production layouts and subsequent capacity planning of the employees are missing the information, methods and tools for making clear decisions. The production costs in general and especially the costs for the workforce create a large part of the operating costs in many manufacturing enterprises. The scientific goal of the article is to present a design of the system for parametric modelling and assessing the working utilisation of the production staff intended for reducing costs. The described solution is based on object-oriented analysis and contains a methodology of planning and controlling the production process in the industrial environment. The designed methodology was used for developing a planning module of project software and was shown through a case study in a company dealing with the production of automotive components. Effective modelling of the digital copy of the manufacturing system in the software environment is one of the most difficult and important steps for developing reliable information systems for planning and inspection in the industry. The methodology's results in a company are that the solution can be used as a basis for the aggregate production planning that brings savings and efficiency increases. The research results can be used in any company with strictly defined working positions, working activities, and limiting conditions. [ABSTRACT FROM AUTHOR]

The flexibility claimed by the next generation production systems induces a deep modification of the behaviour and the core itself of the control systems. Over-connectivity and data management abilities targeted by Industry 4.0 paradigm enable the emergence of more flexible and reactive control systems, based on the cooperation of autonomous and connected entities in the decision-making process. From most relevant articles extracted from existing literature, a list of 10 key enablers for Industry 4.0 is first presented. During the last 20 years, the holonic paradigm has become a major paradigm of Intelligent Manufacturing Systems. After the presentation of the holonic paradigm and holon properties, this article highlights how historical and current holonic control architectures can partly fulfil Industry 4.0 key enablers. The remaining unfulfilled key enablers are then the subject of an extensive discussion on the remaining research perspectives on holonic architectures needed to achieve a complete support of Industry 4.0. [ABSTRACT FROM AUTHOR]

This article presents an approach to the assessment of operational manufacturing systems complexity based on the irregularities hidden in manufacturing key performance indicator time-series by employing three complementary algorithmic complexity measures: Kolmogorov complexity, Kolmogorov complexity spectrum's highest value and overall Kolmogorov complexity. A series of computer simulations derived from discrete manufacturing systems are used to investigate the measures' potentiality. The results showed that the presented measures can be used in quantitatively identifying operational system complexity, thereby supporting operational shop-floor decision-making activities. [ABSTRACT FROM AUTHOR]

Featured Application: This research work proposes a framework for the evaluation of the initial adoption phase of new production technologies and its application to the introduction of a semi-automatic packaging machine in a relevant logistics company. The case study allowed the assessment of the machine ramp-up phase and provided valuable insights for reducing the length of this period by achieving a stable target production output. Moreover, it shows how the framework can be adopted, applied and interpreted for obtaining useful insights. Manufacturing systems in digital and interconnected industrial settings where human worker activity is required represents further potential applications of this paper. The initial adoption phase of new production technologies is the period between the first production run or technology reconfiguration and the achievement of a stable target output. This time frame is generally characterized by productivity unsteadiness, quality performance variability, and unexpected machine failures together with increasing production volumes due to the process setup and instability, which inevitably affects production output. In this context, human performance represents an additional source of variability and process instability that is dependent on the workers' productivity, learning curve and related training activities. Hence, to effectively assess the ramp-up phase of new production technologies, an appropriate evaluation of human performance is required. This paper proposes a comprehensive framework and criteria to perform a consistent assessment of the initial adoption phase of new production technologies by introducing two OEE measurement methodologies that distinguish between human performance, process configuration and technical features of the production technology. The proposed framework is then applied to and validated by a case study concerning the introduction of a semi-automatic packaging machine in a primary multinational company in the logistics industry. This case study shows the difference between the two OEE measures, along with the values interpretation and useful insights for achieving a stable production output. [ABSTRACT FROM AUTHOR]

The layout of machines in a manufacturing system has a significant impact on production time and cost. The aim of a good layout is generally determining the machines' position on the shop floor to minimize transportation time and cost. However, not only machines' positions but also aisle affect transportation cost and time. The aisles are paths that transporters go through them to move the materials between machines. The capacity of the aisles is limited to a specific number of transporters and this may cause transporters to wait before being allowed to pass an aisle. Therefore, when optimizing the layout of machines, the aisles structure and their capacity must be considered. This article presents a hybrid approach for layout design in manufacturing systems taking into account capacitated aisles structure. The proposed approach combines a metaheuristic algorithm and simulation. First, the aisle structure is defined and then, the layout of the machines is determined. Each layout is evaluated through a simulation model. In this way, it is possible to avoid unrealistic assumptions and consider realistic conditions such as stochastic demand, random process time and random transportation time. Finally, a numerical example is included to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

Additive manufacturing processes, especially those based on fused filament fabrication mechanism, have a low productivity. One solution to this problem is to adopt a collaborative additive manufacturing system that employs multiple printers/extruders working simultaneously to improve productivity by reducing the process makespan. However, very limited research is available to address the major challenges in the co-scheduling of printing path scanning for different extruders. Existing studies lack: (i) a consideration of the impact of sub-path partitions and simultaneous printing of multiple layers on the multi-extruder printing makespan; and (ii) efficient algorithms to deal with the multiple decision-making involved. This article develops an improved method by first breaking down printing paths on different printing layers into sub-paths and assigning these generated sub-paths to different extruders. A mathematical model is formulated for the co-scheduling problem, and a hybrid algorithm with sequential solution procedures integrating an evolutionary algorithm and a heuristic is customized to multiple decision-making in the co-scheduling for collaborative printing. The performance was compared with the most recent research, and the results demonstrated further makespan reduction when sub-path partition or the simultaneous printing of multiple layers is considered. This article discusses the impacts of process setups on makespan reduction, providing a quantitative tool for guiding process development. [ABSTRACT FROM AUTHOR]

Currently, manufacturing enterprises attach great importance to improving processing quality and customer satisfaction. Hybrid flow shops have widespread applications in real-world manufacturing systems such as steel production and chemical industry. In a practical production process, uncertainty commonly arises due to the difficulty of knowing exact information of facilities and jobs beforehand. In order to improve processing quality and customer satisfaction of manufacturing systems in uncertain environments, this article proposes a stochastic multiobjective hybrid flow shop scheduling problem aiming at maximizing processing quality and minimizing total tardiness, where the processing time of jobs obeys a known random distribution. To describe jobs’ processing quality mathematically, a quality-based cost function is presented, and further a chance-constrained programming approach is used to formulate this problem. Then, a multiobjective artificial bee colony algorithm incorporating a stochastic simulation approach is designed by considering its characteristics. Simulation experiments are performed on a set of instances and several state-of-the-art multiobjective optimization algorithms are chosen as peer approaches. Experiment results confirm that the proposed algorithm has an excellent performance in handling this problem. [ABSTRACT FROM AUTHOR]

Facility layout is a critical aspect of operations management, since it affects the efficiency and effectiveness of the production process. The facility layout problem (FLP) is a combinatorial optimization problem that involves finding an optimal arrangement of machines, workstations, and material handling systems within a manufacturing facility. FLP is known to be a NP-complex problem for which numerous research studies have been published. This paper presents a survey of the most recent advancements in FLP as an attempt to deal with the current and future trends of research based on well-known papers of the last 16 years. The adopted approach in this paper is to classify FLP according to the environment type, namely, static facility layout problem (SFLP) and dynamic facility layout problem (DFLP) and to investigate the various formulations, application domains and solution methodologies for each classification. Finally, a comparison between all approaches is provided for each classification. This review provides a comprehensive overview of FLP literature and serves as a valuable resource for researchers and practitioners interested in the design and optimization of facility layouts. [ABSTRACT FROM AUTHOR]

Optimization on the basis of sustainability brings important benefits to manufacturing process as sustainable productions constitute a crucial aspect in modern manufacturing. This paper presents a new formalized framework for optimizing the sustainability of manufacturing processes. Unlike previous approaches, the proposed technique combines a methodology for selecting the sustainability indicators and a multi-objective optimization for improving the three sustainability pillars (economy, environment and society). While selecting the significant sustainability indicators in the considered manufacturing process relies on the ABC judgment method, the Saaty's method enables weighting the chosen indicators in order to combine them into suitable economic, environmental and social sustainability indexes. Other technological aspects, usually taken as objectives in previous works, are considered constraints in the proposed approach. The optimization is performed by using nature inspired heuristics, which return the set of non-dominated solutions (also known as Pareto front), from which the most convenient alternative is chosen by the decision maker, depending on the specific conditions of the process. For illustrating the usage of the proposed framework, it is applied to the optimization of a submerged arc welding process. Compared with currently used welding parameters, the computed optimal solution outperforms the economic and environmental sustainability while keeps equal the social impact. The results show not only the effectiveness of the proposed approach, but also its flexibility by giving a set of possible solutions which can be chosen depending on how are ranked the sustainability pillars.

Manufacturing is facing a host of new security challenges due to the convergence of information technology (IT) and operational technology (OT) in the industry. This article addresses the challenges that arise due to the use of low power Industrial Internet of Things (IIoT) devices in modular manufacturing systems of Industry 4.0. First, we analyze security challenges concerning the manufacturing execution system (MES) and programmable logic controllers (PLC) in IIoT through a selective literature review. Second, we present an exploratory case study to determine a protocol for cryptographic key management and key exchange suitable for the Smart Production Lab of Aalborg University (a learning cyber-physical factory). Finally, we combine the findings of the case study with a quality function deployment (QFD) method to determine design requirements for Industry 4.0. We identify specific requirements from both the high-level domain of factory capabilities and the low-level domain of cryptography and translate requirements between these domains using a QFD analysis. The recommendations for designing a secure smart factory focus on how security can be implemented for low power and low-cost IIoT devices. Even though there have been a few studies on securing IT to OT data exchange, we conclude that the field is not yet in a state where it can be applied in practice with confidence.

Internet of Things (IoT) is extending quite rapidly into the physical and virtual manufacturing worlds. IoT facilitates digitalization of manufacturing systems, which are considered vitally important to enhance the effectiveness and efficiency in the future manufacturing era. In order to compete globally and to meet the rapid market changes, manufacturing companies should consider implementation of manufacturing systems that are self-organized and decreasing constant human intervention to a minimum, still keeping the process under human control. In this paper, authors used the concept of Industry 4.0 to upgrade the manufacturing system according to the modern manufacturing needs. Manufacturing systems of Industry 4.0 are called cyber-physical production systems (CPPSs). On the other hand, modelling technologies such as process and data modelling, help to establish and understand the performance of a production system, as the evaluation of the control of an automated production system helps to make it reconfigurable and leads to grasping the idea behind the control mechanism of a CPPS. The aim of the paper is to develop a CPPS based on modelling technologies and to propose a concept of upgrading a relatively traditional production system.

The aim of most manufacturing and production factories can be defined as achieving smart and sustainable long-term production systems, which means moving towards strategies defined by Zero-Defect Manufacturing strategies with many areas of improvement, such as: lowering overall cost and energy consumption, amount of scrapped output and wasting raw material and improving overall lead times, production status overview, and planning abilities. These facts were the motivations behind the writing of this paper. The authors considered if the classical architecture of Zero-Defect Manufacturing can be improved by additional tools conventionally used in modern manufacturing. The authors have selected Advanced Planning and Scheduling software tools. To prove that different scheduling methods can have a serious impact on overall production results, we prepared a simple case base comparing different scheduling rules. The theoretical basis for writing this manuscript was prepared by studying classical ZDM methodology and defining the industry gap. The methodology is based on the Zero-Defect Manufacturing architecture, which is essential for high-level implementation in industrial practice. Adding new tools, such as Scheduling and the Industrial Internet of Things, to the classic ZDM architecture improves overall methodology. The impact of different scheduling strategies, which is also described in this study, depends on industry and working conditions. The scheduling rules were compared by several key performance indicators, such as lead time and the number of late/unfinished orders. The study realized, with the practical accent shown this challenge, further research in connection with digitalization.

Climate change mitigation, the goal of reducing CO2 emissions, more stringent regulations and the increment in energy costs have pushed researchers to study energy efficiency and renewable energy sources. Manufacturing systems are large energy consumers and are thus responsible for huge greenhouse gas emissions; for these reasons, many studies have focused on this topic recently. This review aims to summarize the most important papers on energy efficiency and renewable energy sources in manufacturing systems published in the last fifteen years. The works are grouped together, considering the system typology, i.e., manufacturing system subclasses (single machine, flow shop, job shop, etc.) or the assembly line, the developed energy-saving policies and the implementation of the renewable energy sources in the studied contexts. A description of the main approaches used in the analyzed papers was discussed. The conclusion reports the main findings of the review and suggests future directions for the researchers in the integration of renewable energy in the manufacturing systems consumption models. [ABSTRACT FROM AUTHOR]

The paper explores and presents results of an investigation handling design methodologies using the bio-space and biological phenomena combined with the technical space for the development, design and application of products, machine tools, processes and manufacturing systems carried out by a multinational team including Fraunhofer and CIRP fellows. The main biological approach for designing was bio-inspiration. However, links and examples implementing bio-integration and bio-intelligence were also considered and analyzed during the investigation. The paper describes a systematic design procedure starting from requirements and applications related to manufacturing, using well-known theories and experience in engineering, however analyzing and applying biological materials, properties, structures, phenomena and complete systems. Sustainability aspects combined with the immense and ever growing capacity of ICT and digitization possibilities provided new options for design methodologies. A moveable and flexible machine tool was virtually used as a demonstrator to describe the new methodology. It was proved that the ongoing process of learning from nature and applying biological phenomena, structures and materials, together with new technologies such as Additive Manufacturing (AM) can develop new ideas, processes, manufacturing systems, and products with benefits to performance, productivity, efficiency and sustainability. Nature and biological domains can be integrated, can inspire, be implemented or even applied as an intelligent system in the design procedure. [ABSTRACT FROM AUTHOR]

Digitalization and Industry 4.0 are promising developments for solving current technical challenges. However, to fully exploit the potential of digitalization and Industry 4.0, not only manufacturing and information systems must interact, but biological systems too. The interaction of biological, manufacturing and information systems is an emerging research field defined as Biological Transformation in manufacturing or biologicalisation in manufacturing. This publication introduces three different projects that represent the application of Biological Transformation in manufacturing to inspire and initiate further research in this area. The interactions of the three system types are described in detail, and their potential for the manufacturing sector is discussed in reference to the framework Biological Transformation in manufacturing. [ABSTRACT FROM AUTHOR]

This paper reports on a highly ambitious international study undertaken in the period 2018–2020 on the topic of convergence between biology and advanced manufacturing systems. The international team (authors of this paper) worked together to analyse the status of this convergence through the assessment of concrete examples, referred to here as demonstrators, within advanced manufacturing systems. Four independent demonstrators from different sections of the manufacturing value chain and involving bio-inspiration, bio-integration and/or bio-intelligence were selected to test the following hypothesis: "That Future Manufacturing Systems will incorporate Components, Features, Characteristics and Capabilities that enable the convergence towards Living Systems". Each of these four demonstrators have succeeded in supporting this hypothesis and in providing clear evidence to confirm that significant performance benefits may be derived through the " biologicalisation " of advanced manufacturing systems. This conclusion is of great significance for the next phases of development of manufacturing science and engineering globally. The evidence reported in this paper provides a robust basis for recommending that a deeper analysis of the implications of biologicalised manufacturing systems be undertaken. As a result of this early stage work, it is concluded that there is a high likelihood that this new convergence will lead to a major paradigm shift in advanced manufacturing. Outstanding opportunities exist for high levels of innovation in the next stages of development of advanced manufacturing processes and systems from the biological perspective. The relationship between the human and the physical manufacturing system will also change and the world of advanced manufacturing will be confronted with many new challenges including important ethical questions. [ABSTRACT FROM AUTHOR]

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The article presents the method for the evaluation of selected manufacturing processes using the analysis of vibration and sound signals. This method is based on the use of sensors installed outside the machining zone, allowing to be used quickly and reliably in real production conditions. The article contains a developed measurement methodology based on the specific location of microphones and vibration transducers mounted on the tested object, in this case on a four-axis CNC ST20Y Haas lathe. A mobile phone was integrated into the measuring system and used to control the measurement process. The results from the analysis of vibration and sound signals recorded during different machining operations are presented. They refer to selected working conditions of a machine tool depending on switching the coolant supply on or off and different machine loads caused by various technological processing as well as the various speed of the positioning movements. The analysis was carried out using selected point measures describing the vibroacoustic signals. The synthesis conducted on the basis of results from the experiments indicates the validity of using vibration and acoustic signals, recorded outside the machining area, to evaluate material removal processes that are diverse in terms of kinematics and processing conditions. It indicates the possibility of using proposed point measures of vibroacoustic signals in the diagnostic aspects of the machine tools to achieve high dimension and shape accuracy and to evaluate the condition of the technological devices in terms of their optimal efficiency. Presented methodology can be used as a supporting tool in the CAD/CAM software for a better selection of appropriate cutting parameters and for a wireless control of manufacturing systems consisting of several machine tools. [ABSTRACT FROM AUTHOR]

A maximally permissive (or optimal) supervisory control of an automated manufacturing system (AMS) modeled by Petri nets (PNs) can be usually implemented by imposing constraints in the form of a set of linear inequalities. To find such a set of linear constraints, in the existing work, an integer linear programming (ILP) problem is generally formulated and solved for some dominant markings obtained by reachability analysis and vector covering theory, which is computationally inefficient due to the combinatorial nature of solving ILPs to decide the coefficients of optimal linear constraints. This paper addresses the deadlock prevention problem for AMSs by developing efficient methods to reduce the computational overhead through the establishment of conditions on deciding the maximal permissiveness of linear constraints imposed on a system. By taking the advantage of structural properties of a PN model under consideration, we identify the most part of minimal covered illegal markings that can be optimally controlled via policies obtained by specific linear inequality constraints. Also, algorithms are developed to implement the proposed policies. The proposed approach can verify the optimality of a linear constraint efficiently without solving ILP problems. A linear programming method is developed to deal with the markings that cannot be processed by the proposed structural analysis. It is shown that for the considered class of PNs, called system of simple sequential processes with resources (S3PR), no mixed ILP problem needs to solve and the computational burden is dramatically reduced. Two examples are employed to demonstrate the efficiency of the developed method. [ABSTRACT FROM AUTHOR]

The mean time between failures (MTBF) and mean time to repair (MTTR) of manufacturing equipment (e.g., machines) are used in every quantitative method for production systems performance analysis, continuous improvement, and design. Unfortunately, the literature offers no methods for evaluating the smallest number of up- and downtime measurements necessary and sufficient to calculate reliable estimates of these equipment characteristics. This article is intended to provide such a method. The approach is based on introducing the notion of $(\alpha, \beta)$ -precise estimates, where $\alpha $ characterizes the estimate’s accuracy and $\beta $ its probability. Using this notion, this article evaluates the critical number, $n^{*} (\alpha,\beta)$ , of up- and downtime measurements necessary and sufficient to calculate $(\alpha, \beta)$ -precise estimates of MTBF and MTTR. In addition, this article derives a probabilistic upper bound of the observation time required to collect $n^{*} (\alpha,\beta)$ measurements. Note to Practitioners—To evaluate and predict production systems behavior, managers of manufacturing operations need to know equipment reliability characteristics. Quantifying the equipment status by MTBF and MTTR, this article provides answers to the following questions: Q1: How many measurements of machines up- and downtime are required to obtain reliable estimates of MTBF and MTTR? Q2: How long the observation period must be to collect the desired number of measurements? The answer to Q1 is provided by a rule (formula), which is based on the desired estimate accuracy (characterized by $\alpha $) and its likelihood (quantified by $\beta $). The answer to Q2 consists of selecting a small number of initial measurements, which can be used to calculate an upper bound of the total observation time. [ABSTRACT FROM AUTHOR]

The evolution of manufacturing systems, influenced by changes along four axes - products, technology, business strategies and production paradigms - is presented. Adoption of human-centric decision making in meshed collaboration with intelligent systems is examined. Implications and preparedness for the shift towards more responsive, intelligent adaptive systems are reviewed. Research and industrial use cases are presented. A vision for the new future Adaptive Cognitive Manufacturing System (ACMS) paradigm and its characteristics, drivers and enablers are articulated highlighting the digital and cognitive transformations. Perspectives and insights are offered for future research, education, and work to realize the evolution of manufacturing systems. [ABSTRACT FROM AUTHOR]

The paper is devoted to the mathematical problems of building control systems of modern and promising production systems that ensure the adaptation of production processes to changes in the external and internal conditions of the system. The proposed algorithm for solving the scheduling problem is based on a modification of the algorithm for finding the shortest path between the vertices of the graph. It is a simple and effective tool for solving the problem of minimizing the changeover time. Its effectiveness has been proven. The algorithm has received an adaptive extension when applied in cases where it is necessary to take into account random influences on the production process. For this purpose, planning algorithms are considered, taking into account the actual noise and interference. Computer algorithms for production planning using a system of correction tolerances for the required work schedules and equipment loading are synthesized. [ABSTRACT FROM AUTHOR]

For an assembly manufacturing system, its capacity planning problem and production control problem are usually considered separately. However, production capacity cannot exert its maximum productivity without employing optimal production control; and optimal production control may not satisfy the demand without sufficient production capacity. Therefore, it is important to jointly optimize capacity planning and production control. First, the mathematical formulation of integrated production control and capacity planning problem for an assembly manufacturing system with multiple upstream production lines and one downstream assembly line is proposed in this paper. Second, we develop a method to obtain the marginal probability distribution of the inventory level in the assembly system, which can be employed to calculate the average production cost under an approximate optimal production control policy. Since optimal control policy is subject to production capacity (i.e., machine number), we employ the integer programming method to optimize the machine number (i.e., production capacity), which consequently influences the control policy. Numerical experiments are conducted to verify the correctness of the method of calculating steady-state probability distribution, and the effectiveness of the algorithm of integrated optimization of production control and production capacity. [ABSTRACT FROM AUTHOR]

This article investigates the hierarchy of the manufacturing system, which consists of a set of interrelated processes aimed at converting information, knowledge, energy, materials, and other resources into value for the consumer based on the principles of lean production. Modern manufacturing systems are becoming more and more complex to manage. The problems that need to be solved are associated with a significant number of time-varying parameters, large time delays, high non-linearity of processes, and a complex relationship between input and output parameters. Depending on the parameters of internal components and characteristics of external conditions, the state of manufacturing systems can change in an unpredictable manner. The paper considers many types of discrete states in which the system can be. The estimation of the probability of finding the manufacturing system in any of the given states was carried out using discrete Markov analysis. The article also presents the results of studies of possible transitions between states in which the production system is presented in the form of a transition matrix. [ABSTRACT FROM AUTHOR]

This paper uses a multiple case-study methodology to investigate complexity transfer (CT) in manufacturing supplier-customer systems, leading to a new model of complexity transfer. An entropic-related complexity measure is applied to three supplier-customer systems, internally within each organisation and at their supplier-customer interface. The results are compared and integrated to provide cross-case analyses and insights. Although CT has been acknowledged in the literature to occur towards upstream supply chain (SC) partners, e.g. in the context of the bullwhip effect, this paper provides evidence that CT may also occur towards downstream SC partners. This study also highlights that complexity can be managed through significant and sustained operational interventions. Our new empirically-tested model of CT identifies four organisational types: Sink, Source, Equilibrium, and Boom or Bust, according to their transfer of internally-generated and externally-accepted complexity. This new model enables an in-depth representation of the transfer of complexity and of its impact on SC partnerships. Managers may use this CT model to develop complexity management insights and to identify structural and operational changes – at organisational level, and systemic SC changes that may reduce the costs associated with complexity. [ABSTRACT FROM AUTHOR]

Graphs are powerful tools to model manufacturing systems and scheduling problems. The complexity of these systems and their scheduling problems has been substantially increased by the ongoing technological development. Thus, it is essential to generate sustainable graph-based modeling approaches to deal with these excessive complexities. Graphs employ nodes and edges to represent the relationships between jobs, machines, operations, etc. Despite the significant volume of publications applying graphs to shop scheduling problems, the literature lacks a comprehensive survey study. We proposed the first comprehensive review paper which (1) systematically studies the overview and the perspective of this field, (2) highlights the gaps and potential hotspots of the literature, and (3) suggests future research directions towards sustainable graphs modeling the new intelligent/complex systems. We carefully examined 143 peer-reviewed journal papers published from 2015 to 2020. About 70% of our dataset were published in top-ranked journals which confirms the validity of our data and can imply the importance of this field. After discussing our generic data collection methodology, we proposed categorizations over the properties of the scheduling problems and their solutions. Then, we discussed our novel categorization over the variety of graphs modeling scheduling problems. Finally, as the most important contribution, we generated a creative graph-based model from scratch to represent the gaps and hotspots of the literature accompanied with statistical analysis on our dataset. Our analysis showed a significant attention towards job shop systems (56%) and Un/Directed Graphs (52%) where edges can be either directed, or undirected, or both, Whereas 14% of our dataset applied only Undirected Graphs and 11% targeted hybrid systems, e.g., mixed shop, flexible, and cellular manufacturing systems, which shows potential future research directions. [ABSTRACT FROM AUTHOR]

The business environment is increasingly complex and competitive so organizations must respond to different market conditions by reconfiguring their processes, products and services. The objective of this article is based on a literature review under a scientometric and bibliometric approach, referring to the complexity in manufacturing systems, allowing to find answers to relevant questions with respect to the subject matter and to show literally the scientific fields of application, factors of complexity and methods of solution. Methodologically, a first stage is developed on the problems and formulation of the research questions, the second stage establishes the information search strategy and finally a statistical analysis is carried out. The findings show a positive trend and interest on the part of the scientific community in the number of publications related to the subject in the different databases, highlighting the exercise of the theory of complexity in different disciplines but on a smaller scale in scientific fields such as manufacturing and supply chain management. [ABSTRACT FROM AUTHOR]

Multi-product systems with finite buffers and sequence-dependent set-up times are quite common in modern manufacturing industry. In practice, the distribution of machine processing time could be arbitrary, while in existing literature it is often assumed to follow an exponential distribution. In this paper, we develop an analytical method to study the multi-product manufacturing systems with non-exponential processing times. An embedded Markov chain model is constructed and two approximation methods, Gamma estimation and linear approximation, are proposed. The model is validated with high accuracy by numerical experiments and practical data from an automotive assembly system. [ABSTRACT FROM PUBLISHER]

In this paper, we refer to a real case study of an industrial partner recently committed to its project on the design of a multi-unit and multi-product manufacturing system. Although the considered problem refers to an actual complex manufacturing system, it can be theoretically classified as a union of two key problems that need to be solved during the transfer line design stage: the transfer line balancing problem (TLBP) and the buffer allocation problem (BAP). As two closely related problems, TLBP and BAP usually have similar optimizing directions and share the same purpose: finding a balance between the performance of the transfer line system as well as minimizing investment costs. These problems are usually solved sequentially, but this leads to solutions close to a local optimum in the solution space and not to the global optimum of the overall problem. This paper presents a multi-objective optimization for concurrently solving transfer line balancing and buffer allocation problems. The new approach is based on a combination of evolutionary and heuristic-based algorithms and takes into account the uncertainty of market demand. To validate the proposed approach, an industrial case study in a multi-unit manufacturing system producing multiple products (four engine blocks) is discussed.

Asynchronous systems with deterministic processing times are critical for fabricating discrete parts, especially in countries offering high salaries. Despite their widespread application, it is difficult to analyze these systems using conventional analytical modeling tools as commonly used hypotheses for these models consider different conditions compared to real behaviors. This paper presents a new analytical model for the performance evaluation of asynchronous two-stage manufacturing lines fabricating discrete parts with finite buffer capacity. A continuous-time Markov chain flow model that mimics the behavior of discrete asynchronous manufacturing systems by employing threshold-based control mechanisms is presented. The results obtained with the proposed model are compared with those using discrete event simulation, and they show good agreement. The model proposed in this paper can be used as a building block for the evaluation of longer asynchronous lines. [ABSTRACT FROM AUTHOR]