Complex Manufacturing Systems Research Articles

A multi-objective approach for resilience-based system design optimisation of complex manufacturing systems

Disruptive events in complex manufacturing systems (CMS), characterised by labour-intensive processes and repetitive activities, render these systems vulnerable. In order to tackle this challenge, an approach for resilience-based system design optimisation is proposed. The approach: (i) introduces a dynamic multi-dimensional resilience metric; and (ii) formulates the resilience as a multi-objective optimisation problem to improve CMSs resilience by finding an optimal human resource allocation model, considering design factors including redundancy, resources capacity and roles. The case study, selected to test the validity of the presented approach, show improvement in resilience and efficiency, in terms of throughput, resources utilisation and restoration time.

Data-based improvement of engineering change impact analyses in manufacturing

Due to dynamic business environments and volatile customer requirements, manufacturing companies are increasingly relying on agile development methods to implement changes to their products more quickly. The drive towards speed, however, is in conflict with the thorough analysis of engineering change impacts on more and more complex manufacturing systems. In this context, efficient change management that enables rapid processing of changes while maintaining consistently high product quality is key for competitiveness. This paper examines existing literature and presents a concept for the data-based improvement of engineering change impact analyses in manufacturing.

Towards Long Time Growth in Equilibrium: Game Approach

Different levels of development of individual countries cannot be simply explained with some of them enjoying better natural conditions. After all, so-called paradise islands haven’t become leaders of growth, while a number of countries with difficult natural conditions (Scandinavian states, Japan) rank within strict leaders of the civilized world and there are hardly any signs to suggest they are about to lose their positions. This makes them models others are encouraged to follow. However, it occurs that the rate of success in implementation of selected solutions therefrom is rather mediocre. From historical perspective this situation is quite new. Assembly lines in Ford factories could have been effectively applied worldwide. Fast-food networks or shipping containers could have too. At present, however, in the 21^st Century, equally spectacular cases seem hard to find. This may come as a surprise, considering that striking differences persist in the efficiency of individual countries’ economies. In Europe, the distance between the North and the South remains undiminished; enormous chasm divides the United Mexican States from the United States of America. And all this happens in the world where – apart from few exceptions – almost all economies are open to well-proven solutions. Even if it’s impossible to transform the whole thing over a short period, modeling upon best examples and practices, still it should be viable to achieve a gradual progress, step by step, through adaptation and transformation of individual elements of a given economy and emulating model solutions taken, for example, from neighbor countries. This way the development gap could be slowly reduced or at least stopped from increasing any further. And yet, this approach fails to yield significant effects. The reason, it seems, is in the growing integration and complexity of national manufacturing systems. It is always difficult to build something into a compact and complex system. This is especially evident with attempts to adapt foreign innovation-fostering systems. Usually, due to the lack of some elements, links, etc., the sub-system in question cannot achieve the efficacy of its model. Does it mean that such an implementation is entirely infeasible? No, it’s not. While inflexible top-down adaptation occurs ineffective, it may be possible to start a successful process of self-adaptation that reaches deep within the entire system. It could be imagined like veins of the right kind of mold that penetrates cheese making it delicious. The point is this is a difficult sort of operation that requires a non-traditional (non-static) systemic approach.

Literature review and prospect of the development and application of FMEA in manufacturing industry

In order to enable failure mode and effects analysis (FMEA) to play a better quality control role in complex manufacturing products or systems, the current research status of FMEA is reviewed from failure mode identification, risk assessment, and industrial standard application. Firstly, the research status of system failure identification is summarized from the following aspects: the breakthrough point of identification, the types of identification methods, and the normalized description of failure modes. Then, sort out the research status of risk assessment from five aspects: risk factor evaluation criteria, risk assessment opinion expression, expert opinion consensus, risk opinion assessment aggregation, and sensitivity analysis, and find out research hotspots and blind spots; finally, the changes of FMEA standards in various fields are summarized and compared, and the future development trend of FMEA in the context of intelligent manufacturing is discussed.

Innovative framework for lean and green complex manufacturing systems using value stream mapping

Innovative framework for lean and green complex manufacturing systems using value stream mapping

Considerations on Present Production Science Theory and Didactics: The Evident Lack of a Rational Manufacturing Theory

Today’s universities’ manufacturing courses on production system design and analysis of performance dynamics are heuristics than rational-based, far away from a physics or mathematics theorem constructed science. This is also due to increasing computational power, which allowed the proliferation of simulation-based modeling for complex manufacturing systems to analyze their behavior and optimize their performance. Indeed, manufacturing theory remained stuck to generic practice-based production concepts. The lack of a well-structured scientific production specific theory is increasingly problematic. Engineering students lacking the foundation of a solid theory will base manufacturing systems design rather on believing that the optimization software will solve the scheduling issue instead of comparing alternative modern design principles to conceive high performance manufacturing systems. This attitude is far away from a scientific rational approach to design demand characteristic and product appropriate manufacturing systems by knowing “the theory”. A scientifically formulated manufacturing theory should cover two aspects: Firstly, a proper Cartesian-based understanding and law-based modeling of manufacturing systems to describe rationally their behavior; and secondly, a library with a comprehensive set of elementary production systems’ design principles. The design principles define the functioning of the manufacturing system and the theory models the resulting dynamics of the manufacturing system. This paper exemplarily shows the increased didactic and professional benefit of such an appropriate, solid, theorem and law based manufacturing science. In addition, it also discusses the impact of presently applied manufacturing simulation in today’s context of emerging Industry 4.0 type cyber-physical production systems (CPPS).

Innovative framework for lean and green complex manufacturing systems using value stream mapping

Innovative framework for lean and green complex manufacturing systems using value stream mapping

New models and algorithms to solve integrated problems of production planning and control taking into account worker skills in flexible manufacturing systems

The paradigm of the cyber-physical manufacturing system is playing an increasingly important role in the development of production systems and management of manufacturing processes. This paper presents an optimization model for solving an integrated problem of production planning and manufacturing control. The goal is to create detailed production plans for a complex manufacturing system and to control the skilled manual workers. The detailed optimization model of the problem and the developed approach and algorithms are described in detail. To consider the impact of human workers performing the manufacturing primary operations, we elaborated an extended simulation-based procedure and new multi-criteria control algorithms that can manage varying availability constraints of parallel workstations, worker-dependent processing times, different product types and process plans. The effectiveness of the proposed algorithms is demonstrated by numerical results based on a case study.

Fault diagnosis for underdetermined multistage assembly processes via an enhanced Bayesian hierarchical model

Previous works have shown that only relying on measurement data is generally insufficient to identify root causes of the dimensional variation for the complex manufacturing system. It is also well known that for underdetermined multistage assembly processes (MAPs), the number of measurements is less than that of process errors so that the traditional methods are not available for the variation source identification. Therefore, there exists a substantial challenge for fault diagnosis of underdetermined MAPs, especially for the case with multiple fault patterns. To tackle this problem, a novel approach that integrates statistical analysis with domain knowledge is proposed in this paper. First, the variation propagation model is employed to reveal the inherent relationship between key control characteristics and key product characteristics, and then the corresponding variance model is constructed to interpret process faults by means of the abnormal variance of process errors. Considering that the probability of less process faults is far higher than that of more process faults in MAPs, the problem of fault diagnosis is further transformed into searching the sparse solution of abnormal variance changes for process faults. Afterwards, based on the non-negative property of the covariance matrix, an enhanced Bayesian hierarchical model is developed to favor sparse estimation of the variance for the underdetermined system in MAPs. Finally, experimental results of both the numeric simulation and practical case study demonstrate the proposed diagnosis methodology can effectively identify the process faults for different patterns with system noise.

Linkage between TPM, people management and organizational performance

PurposeThe purpose of this paper is to explore an empirical investigation of the linkage between total productive maintenance (TPM), people management (PEM) and organizational performance in the apparel industry of Bangladesh. This study also explores the direct and mediating effect of TPM on organizational performance.Design/methodology/approachEmpirical data were collected using a close-ended structured questionnaire from 227 small, medium and large-scale garment manufacturing industries. The causal relationships between PEM, TPM and organizational performance were examined using structural equation modeling.FindingsThe findings of this study have provided significant evidence that the PEM has both direct and indirect impact (through TPM) on organizational performance. The results of the analysis also indicated that TPM has direct and mediating effects on operational performance.Research limitations/implicationsData for measuring PEM practices and organizational performance were collected from the same source, assuming that PEM practices are uniform across the organization. Therefore, response bias is not unexpected.Originality/valueThis study explores the interdimensional linkage between PEM, TPM and organizational performance and argues based on the empirical data in the context of the apparel industry in Bangladesh. The apparel industry is a complex manufacturing system synchronizing man, machine and material. The findings provide empirical evidence that employee engagement and practice of TPM will enhance the operational performance of the apparel industries.

Improved multi-fidelity simulation-based optimisation: application in a digital twin shop floor

ABSTRACT In recent years, the literature has paid considerable attention to digital twin technology for the implementation of Industry 4.0 and intelligent manufacturing. Most of the literature argues that simulation models are a key platform for digital twins and considers discrete-event simulation to be a suitable method to model real dynamic manufacturing systems. However, the discrete-event simulation of complex manufacturing systems is a time-consuming process. Therefore, it is difficult to deal with the large-scale discrete optimisation problems in digital twin shop floors. To bridge this research gap, we propose an improved multi-fidelity simulation-based optimisation method based on multi-fidelity optimisation with ordinal transformation and optimal sampling (MO2TOS) in the current research. The proposed method embeds heuristic algorithms to accelerate the solution space search efficiency in MO2TOS. Moreover, we develop an improved multi-fidelity simulation-based optimisation system by integrating the proposed method with discrete-event simulation tools and apply this system to a digital twin-based aircraft parts production workshop. Based on this digital twin shop floor, we conduct different production planning experiments to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed improved multi-fidelity simulation-based optimisation method is well-applied in solving large-scale problems and outperforms other simulation-based optimisation methods.

Quality monitoring of complex manufacturing systems on the basis of model driven approach

Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.

Assessing operational complexity of manufacturing systems based on algorithmic complexity of key performance indicator time-series

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.

Real-time production scheduling in the Industry-4.0 context: Addressing uncertainties in job arrivals and machine breakdowns

The utilization of real-time information in production scheduling decisions becomes possible with the help of new developments in Information Technology and Industrial Informatics, such as Industry 4.0. Regardless of the beliefs that the availability of such information will enhance scheduling decisions, several questions and concerns have been reported. One such question is to what extent can the availability of real-time information enhance scheduling decisions? Another concern is how can such information be utilized to advance scheduling decisions and when should it be used? Moreover, there is a general assumption that continuous rescheduling using real-time system updates is beneficial to some extent. However, this general assumption has not been extensively investigated in complex manufacturing systems, such as flexible job shops. Therefore, in this paper, our objective is to study the above-mentioned research questions by developing real-time scheduling (RTS) models for the flexible job-shop scheduling problem (FJSP) with unexpected new job arrivals and machine random breakdowns. We investigate how real-time updates on unexpected arrivals, the availability of machines (downtimes and recovery times), and the completion times of operations can be utilized to generate new schedules (i.e., rescheduling). The performance of the developed RTS models is also investigated under different settings for shop-floor events, different rescheduling strategies, rescheduling policies, and scheduling methods. Lastly, results, conclusions, and several promising research avenues are provided.

Preventive maintenance scheduling optimization based on opportunistic production-maintenance synchronization

Equipment maintenance is momentous for improving production efficiency, how to integrate maintenance into production to address uncertain problems has attracted considerable attention. This paper addresses a novel approach for integrating preventive maintenance (PM) into production planning of a complex manufacturing system based on availability and cost. The proposed approach relies on two phases: firstly, this study predicts required capacity of each machine through extreme learning machine algorithm. Based on analyzing historical data, the opportunistic periods are calculated for implementing PM tasks to have less impact on production and obtain the PM interval and duration. Secondly, this study obtains the scheduling planning and the least number of maintenance personnel through an improved ant colony optimization algorithm. Finally, the feasibility and benefits of this approach are investigated based on empirical study by using historical data from real manufacturing execution system and equipment maintenance system. Experimental results demonstrate the effectiveness of proposed approach, reduce personnel number while guarantee the maintenance tasks. Therefore, the proposed approach is beneficial to improve the company’s production efficiency.

Dual mode control strategy for the energy efficiency of complex and flexible manufacturing systems

The manufacturing industry is shifting towards smart manufacturing, in which both energy efficiency and flexibility are some of the main objectives of this digital transformation. In this regard, the control strategies for manufacturing systems should be able to support the requirements of this transformation with a low computational burden towards their implementation in real time. To this end, in this paper, a dual mode control strategy based on two control approaches is proposed to minimise the energy consumption of manufacturing systems without affecting their productivity, even when scenarios of flexible manufacturing are considered. The first control mode is based on model predictive control to determine an optimisation-based strategy for the constrained behaviour of the system. Then, the second mode builds on the assumption that the system exhibits a periodic behaviour and, thus, it will be able to switch to an autonomous control mode that avoids the resolution of an optimisation problem online. The proposed control strategy is tested in a manufacturing process line in which changes in the production programs are considered with the aim to test the performance in flexible manufacturing scenarios. The obtained results show that the computational burden could be significantly reduced while reducing global energy consumption without affecting the system productivity.

How to model and implement connections between physical and virtual models for digital twin application

Digital twin (DT) is a virtual mirror (representation) of a physical world or a system along its lifecycle. As for a complex discrete manufacturing system (DMS), it is a digital model for emulating or reproducing the functions or actions of a real manufacturing system by giving the system simulation information or directly driven by a real system with proper connections between the DT model and the real-world system. It is a key building block for smart factory and manufacturing under the Industry 4.0 paradigm. The key research question is how to effectively create a DT model during the design stage of a complex manufacturing system and to make it usable throughout the system’s lifecycle such as the production stage. Given that there are some existing discussions on DT framework development, this paper focuses on the modeling methods for rapidly creating a virtual model and the connection implementation mechanism between a physical world production system at a workshop level and its mirrored virtual model. To reach above goals, in this paper, the discrete event system (DES) modeling theory is applied to the three-dimension DT model. First, for formally representing a manufacturing system and creating its virtual model, seven basic elements: controller, executor, processor, buffer, flowing entity, virtual service node and logistics path of a DMS have been identified and the concept of the logistics path network and the service cell is introduced to uniformly describe a manufacturing system. Second, for implementing interconnection and interaction, a new interconnection and data interaction mechanism between the physical system and its virtual model for through-life applications has been designed. With them, each service cell consists of seven elements and encapsulates input/output information and control logic. All the discrete cells are constructed and mapped onto different production-process-oriented digital manufacturing modules by integrating logical, geometric and data models. As a result, the virtual-physical connection is realized to form a DT model. The proposed virtual modeling method and the associated connection mechanism have been applied to a real-world workshop DT to demonstrate its practicality and usefulness.

A budget allocation strategy minimizing the sample set quantile for initial experimental design

The increased complexity of manufacturing systems makes the acquisition of the system performance estimate a black-box procedure (e.g., simulation tools). The efficiency of most black-box optimization algorithms is affected significantly by initial designs (populations). In most population initializers, points are spread out to explore the entire domain, e.g., space-filling designs. Some population initializers also consider exploitation procedures to speed up the optimization process. However, they are either application-dependent or require an additional budget. This article proposes a generic method to generate, without an additional budget, several good solutions in the initial design. The aim of the method is to optimize the quantile of the objective function values in the generated sample set. The proposed method is based on a clustering of the solution space; feasible solutions are clustered into groups and the budget is allocated to each group dynamically based on the observed information. The asymptotic performance of the proposed method is analyzed theoretically. The numerical results show that, if proper clustering rules are applied, an unbalanced design is generated in which promising solutions have higher sampling probabilities than non-promising solutions. The numerical results also show that the method is robust to wrong clustering rules.

Enhancement of Computational Efficiency in Seeking Liveness-Enforcing Supervisors for Advanced Flexible Manufacturing Systems with Deadlock States

In industry 4.0, all kinds of intelligent workstations are designed for use in manufacturing industries. Among them, flexible manufacturing systems (FMSs) use smart robots to achieve their production capacity under the condition of a high degree of resources sharing. As a result, deadlock states usually appear unexpectedly. For solving the damage deadlock problem, many pioneers have proposed new policies. However, it is very difficult to make systems maximally permissive even if their policies can solve the deadlock problem of FMSs. According to our survey, the Maximal number of Forbidding First Bad Marking (FBM) Problems (MFFP) seems to be the best technology to obtain systems’ maximally permissive states in the existing literature. More importantly, the number of added control places (CP) is the smallest among the existing research works. However, when the complexity of a flexible manufacturing system increases, the computational burden rises rapidly. To reduce computational cost, we define a new concept named Pre Idle Places (PIP) to enhance the computational efficiency in Seeking Liveness-Enforcing Supervisors. We can bypass all PIP once they can be identified from a deadlock system under the process of solving MFFP. According to the data showed in three classical examples, our proposed Improved MFFP is better than conventional MFFP in terms of computational efficiency with the same controllers.

Robust parallel-batching scheduling with fuzzy deteriorating processing time and variable delivery time in smart manufacturing

Smart manufacturing is an effective way to improve the efficiency of resource utilization and reduce the response time of making joint decisions for the enterprises. Though, with the globalization of manufacturing enterprises, manufacturing optimization problems often occur in complex manufacturing systems under the deteriorating and fuzzy environment, which brings many challenges to smart manufacturing, such as the lack of coordinating scheduling strategies to guarantee the low latency requirement. This paper investigates a robust parallel-batching scheduling problem with fuzzy processing time and past-sequence-dependent delivery time. Some structural properties are first identified, and an optimal algorithm is further developed for the single-machine scheduling problem. Then, the problem is proved to be NP-hard. We thus design a hybrid Multi-Verse Optimizer-Variable Neighborhood Search algorithm to solve the investigated problem in a reasonable time. Abundant experiments of different scales are conducted to verify the performance of the proposed hybrid method with a comparison of the state-of-the-art methods. The proposed hybrid meta-heuristic shows excellent results, robustness, and computational time performance under various experiments.