Modeling Of Manufacturing Systems Research Articles

Guest Editorial Special Section—Papers From the 2019 MASM/WSC Conference

The 2019 International Conference on Modeling and Analysis of Semiconductor Manufacturing (MASM 2019) was part of the 2019 Winter Simulation Conference (WSC) that was held December 8–11 in National Harbor, MD, USA. The MASM conferences provide a forum for the exchange of ideas and industrial innovations between researchers and practitioners from around the world involved in modeling and analysis of complex high-tech manufacturing systems. While the papers from MASM conferences are of high quality and are often cited in journal papers, they are limited in size. As a premier journal for topics covered in the MASM conferences, IEEE T ransactions on S emiconductor M anufacturing , agreed to consider extended article submissions based on the 21 papers accepted and presented at the MASM 2019 conference for a Special Section of an upcoming issue of the journal. were sessions on Scheduling and Dispatching, Fab Scheduling, Maintenance and Engineering, Capacity and Production Planning, Qualification and Wafer-to-Order Allocation, Semiconductor Manufacturing Models, and Semiconductor Modeling Representations. Dr. Sabine Herlitschka, Chief Executive Officer and Chief Technology Officer of Infineon Technologies Austria AG, gave an excellent keynote talk titled “A European View on Future Semiconductor Industry Needs for MASM.” An archive of the MASM 2019 papers can be found at https://informs-sim.org/wsc19papers/by_area.html .

Generalized nets and intuitionistic fuzzy pairs as tools for modelling of flexible manufacturing systems

Generalized nets and intuitionistic fuzzy pairs as tools for modelling of flexible manufacturing systems

Availability Analysis of Machining System with Common Cause Failure, Inspection and Imperfect Repair

This investigation is concerned with the availability prediction of a non-Markovian model for the multi-component complex repairable manufacturing system. The system is prone to multiple types of failures, either due to an individual or common cause failure. The system can also fail catastrophically which may occur due to the incorrect start of the system or due to some errors by an untrained and inexperienced operator. When a system failure occurs, the recovery of the system is done by the inspection/repair process. The stochastic analysis is carried out using the supplementary variable technique and Laplace transformation for evaluating the system state probabilities. The performance indices such as availability, reliability, MTTF, MTTR, and cost–benefit analysis of the system are established analytically and then validated by taking a numerical example.

Simulated annealing and artificial immune system algorithms for cell formation with part family clustering

Cell formation problem (CFP) is one of the main problems in cellular manufacturing systems. Minimizing exceptional elements and voids is one of the common objectives in the CFP. The purpose of the present study is to propose a new model for cellular manufacturing systems to group parts and machines in dedicated cells using a part-machine incidence matrix to minimize the voids. After identifying the exceptional elements, the machines required for processing the remained operations of corresponding parts which are not processed in the dedicated cells are specified. This results in a new matrix called part family-machine. Then, by clustering the part family-machine incidence matrix, the part families that should be assigned to a specific cell to achieve the highest similarity can be determined. The similarity can be translated to sharing machines required for completing the processes and form new cells called shared cells to minimize the number of exceptional elements and voids. Unlike previous models in which the similarity is considered only in the dedicated cells, in the proposed model, the similarity would be monitored and observed in the entire production process. Due to the complexity of our model, two meta-heuristic algorithms including artificial immune system (AIS) and simulated annealing (SA) are proposed. The efficiency of the algorithms is compared to that of exact solutions. Also, the algorithms are compared regarding the quality of solutions. Finally, according to grouping efficacy measure, SA algorithm has a superior performance in comparison with AIS by spending less CPU time.

Energy-efficient Scheduling for Multistage Manufacturing System with Parallel Machines

Increasing energy is prompting academicians to pay attention to energy efficient scheduling. This study establishes a mathematical model for multistage manufacturing system with parallel machines to minimize total energy consumption. A memetic algorithm based on differential evolution(MABDE) is developed to solve the model effectively. Two rules are incorporated into the proposed algorithm to narrow down the search space and improve search efficiency. Computational results demonstrate the effectiveness of the proposed model and algorithm.

Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system

Digital twins can achieve hardware-in-the-loop simulation of both physical equipment and cyber model, which could be used to avoid the considerable cost of manufacturing system reconfiguration if the design deficiencies are found in the deployment process of the traditional irreversible design approach. Based on the digital twin technology, a quad-play CMCO (i.e., Configuration design-Motion planning-Control development-Optimization decoupling) design architecture is put forward for the design of the flow-type smart manufacturing system in the Industry 4.0 context. The iteration logic of the CMCO design model is expounded. Two key enabling technologies for enabling the customized and software-defined design of flow-type smart manufacturing systems are presented, including the generalized encapsulation of the quad-play CMCO model and the digital twin technique. A prototype of a digital twin-based manufacturing system design platform, named Digital Twin System, is presented based on the CMCO model. The digital twin-based design platform is verified with a case study of the hollow glass smart manufacturing system. The result shows that the Digital Twin System-based design approach is feasible and efficient.

Performance evaluation of a carousel configured multiple products flexible manufacturing system using Petri net

This study presents a schematic approach to model, simulate and analyze the overall performance of an FMS, comprising a carousel layout surrounded by manufacturing and assembly cells capable to deliver a variety of multiple products. All manufacturing and assembly cells are consisting of multiple multifunctional production and assembly machines, served by material loading and unloading robots, connected through a material transporting conveyor. Inter-cellular and intra-cellular routing flexibility are employed in the system through production resources operational and breakdown modes. Colored Petri net tools are used to develop a compact, editable and deadlock-free flexible manufacturing system model reveals important information about dynamic behavior and system performance measures e.g.; throughput, mean cycle time, and work-in-process (WIP) with reference to material handling and supply system, production resources reliability and process execution input variables. Variation in input factors namely mean machining and assembly time, material loading and unloading time, number of operations between failure, repair time, parts inter-arrival time, conveyor speed and buffer capacity have shown a substantial effect on the system performance. The results indicate successful applicability of colored Petri net tools for modelling, simulation, and evaluation of carousel configured multiple products flexible manufacturing system. Further, the optimal solution represents a deep relationship between the different production modes and responses to maximize the performance of a carousel configured multiple products FMS.

Optimal enforcement of liveness for decentralized systems of flexible manufacturing systems using Petri nets

The decentralized supervisory structure has drawn much attention in recent years. Many studies are reported in the paradigm of automata while few can be found in the Petri net model. This paper proposes a new method for decentralized supervisory control using the Petri net paradigm. Two efficient Algorithms are developed in the proposed method. Algorithm 1 is used to compute decentralized working zones from the given LS3PR Petri net model for flexible manufacturing systems. Algorithm 2 is used to compute the decentralized controllers that enforced liveness to the decentralized working zones. The sequential assembling is used to reconnect and controlled the working zones via decentralized controllers. The decentralized controller is added to the decentralized working zones that have common elements, that is, common transitions. The proposed method has the following advantages: (i) it can be applied to a complex Petri net model for flexible manufacturing systems, (ii) the proposed methods has less computational complexity when compared with the previous methods, (iii) the proposed method can obtain a minimal number of decentralized controllers that enforce liveness of the uncontrolled Petri net model. Experimental examples are presented to explore the applicability of the proposed methods.

An Integrated Multi-Period Layout Planning and Scheduling Model for Sustainable Reconfigurable Manufacturing Systems

In a dynamic production environment, not only the customer’s needs change with time, but the economic aspects of that environment, such as energy pricing, also change. Reconfigurable Manufacturing Systems (RMSs) are designed to respond to such changes by reconfiguring system components efficiently. This paper presents a novel mathematical model to maximize energy sustainability of RMS. The novelty aspect of the model is the consideration of energy sustainability concurrently with system configuration and scheduling decisions in each period of the planning horizon. The objective of this mixed integer linear model is to minimize the total cost of energy consumption, system reconfiguration, and part transportation between machines, depending on fluctuations of energy pricing and demand during different periods. Several case studies are solved by GAMS Software to illustrate the performance of the presented model and analyze its sensitivity to the volatility of energy pricing and demand to show their effect on system changeability. An efficient genetic algorithm (GA) has been developed to solve the model in larger scale due to its NP-hardness and compared to GAMS for validation. Results show that the presented GA finds near-optimal solutions in 70% shorter time than GAMS on average.

A fuzzy multi-objective model for a cellular manufacturing system with layout designing in a dynamic condition

Cellular manufacturing system (CMS) plays a remarkably significant role in modern production systems. This paper presents a novel and practical fuzzy multi-objective mathematical model for a CMS in a dynamic condition considering the flexibility in allocating machines. The proposed model seeks to determine the best layout design in each production period. Two conflicting objectives are considered including minimising the cost of manufacturing system due to the amount of production loss caused by waste, as well as minimising the variance of fuzzy costs. Due to the complexity of the problem, non-dominate sorting genetic algorithm-II and multi-objective particle swarm optimisation algorithm are designed to solve the model and to obtain the effective solutions. In order to demonstrate the efficiency of the algorithms and to choose the premier algorithm, both algorithms are evaluated in several A fuzzy multi-objective model for a cellular manufacturing system 515 random problems and then compared based on the existing measurement indicators. Then, the algorithms are tuned to solve the problem, based on which their performances are analysed statistically. The applicability of the proposed approach and the solution methodologies are demonstrated as well.

Modelling of flexible manufacturing systems

This paper describes an original approach to one of the key problems in modern mechanical engineering – modelling of modern manufacturing systems with regard to the choice of relevant model type. It is shown that in terms of systemology, all the existing models are divided into the imitating (replicating) and optimising ones, while manufacturing system design belongs to semi-structured issues. Therefore, at the macro-designing stage one should use optimising models, rather than the imitating ones. The study provides a broad definition of the technological process as a set of actions aimed at product manufacturing, designing the sequence of relevant steps. It defines production flexibility and adaptation of the manufacturing system to changing conditions. These solutions can be used in automation in manufacturing, computer aided design (CAD) and modelling (CAM), computer aided engineering analysis, flexible manufacturing systems, flexible automation and production engineering.

Modelling of flexible manufacturing systems

This paper describes an original approach to one of the key problems in modern mechanical engineering – modelling of modern manufacturing systems with regard to the choice of relevant model type. It is shown that in terms of systemology, all the existing models are divided into the imitating (replicating) and optimising ones, while manufacturing system design belongs to semi-structured issues. Therefore, at the macro-designing stage one should use optimising models, rather than the imitating ones. The study provides a broad definition of the technological process as a set of actions aimed at product manufacturing, designing the sequence of relevant steps. It defines production flexibility and adaptation of the manufacturing system to changing conditions. These solutions can be used in automation in manufacturing, computer aided design (CAD) and modelling (CAM), computer aided engineering analysis, flexible manufacturing systems, flexible automation and production engineering.

A fuzzy multi-objective model for a cellular manufacturing system with layout designing in a dynamic condition

Cellular manufacturing system (CMS) plays a remarkably significant role in modern production systems. This paper presents a novel and practical fuzzy multi-objective mathematical model for a CMS in a dynamic condition considering the flexibility in allocating machines. The proposed model seeks to determine the best layout design in each production period. Two conflicting objectives are considered including minimising the cost of manufacturing system due to the amount of production loss caused by waste, as well as minimising the variance of fuzzy costs. Due to the complexity of the problem, non-dominate sorting genetic algorithm-II and multi-objective particle swarm optimisation algorithm are designed to solve the model and to obtain the effective solutions. In order to demonstrate the efficiency of the algorithms and to choose the premier algorithm, both algorithms are evaluated in several A fuzzy multi-objective model for a cellular manufacturing system 515 random problems and then compared based on the existing measurement indicators. Then, the algorithms are tuned to solve the problem, based on which their performances are analysed statistically. The applicability of the proposed approach and the solution methodologies are demonstrated as well.

Reliability Evaluation for Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net

Due to failure, partial failure, or maintenance, the capacity of each machine is multi-state. Therefore, the limited relationship between the capacity of each machine and the input raw materials has to be considered. Additionally, in order to utilize the machine more effectively, the capacity of the buffers cannot be ignored, too. In this paper, a dynamic adaptive fuzzy reasoning Petri net is proposed to evaluate reliability of a manufacturing system with multiple production lines. Firstly, the model of manufacturing system is conducted, and from the perspective of demand, the minimum capacity vector and loading vector of each machine are determined. Secondly, knowledge representation and rules are formulated to establish weighted fuzzy petri nets. And the weighted fuzzy Petri net is adaptive based on the real-time level of buffers, the minimum capacity vector and loading vector. Moreover, the efficiency of product production can be improved while ensuring system reliability by adjusting the buffer level. Finally, a numerical experiment is used to demonstrate the application of our method.

An Integrated Mathematical Programming Model for a Dynamic Cellular Manufacturing System with Limited Resources

This paper proposes an integrated integer nonlinear programming model for a concurrent cell formation and production planning problem in a dynamic cell-manufacturing system (DCMS) with limited resources to setup cells and to procure machines. The proposed model seeks to minimise the total costs associated with the production planning and the cell construction and formation under a dynamic system. To validate the model, it is first converted to a linear programming. Then, a numerical example is presented based on which the branch and bound method is used to solve it employing the Lingo 8 software. Besides, due to NP-hardness of the problem, two meta-heuristic algorithms namely a GA and a PSO are utilised to solve large-scale problems. In addition, the Taguchi method is used to calibrate the parameters of the meta-heuristics in order to find better quality solutions. Computational results confirm the efficiency and the effectiveness of the proposed solution algorithms.

Priced Timed Automata Models for Control of Intelligent Product Agents in Manufacturing Systems

Abstract To enable product customization and incorporate customer requirements during production, there is a need to integrate more flexible control strategies into the shop floor. One type of strategy that has been proposed to address these challenges is multi-agent control. An integral part of this control strategy is the product agent, a controller for an individual part in the manufacturing system. The product agent uses a continuously updated model of the local manufacturing system to make decisions. However, current models used by product agents do not explicitly incorporate the system’s time constraints. This work proposes a priced timed automata modeling framework to represent an environment model for a product agent. The proposed environment model incorporates personalized production requirements and system constraints for the product agent. To make decisions, the product agent solves an optimization problem with the environment model. A semiconductor manufacturing simulation showcases how multiple product agents can use this model to drive the production of customized parts.

An integrated mathematical programming model for a dynamic cellular manufacturing system with limited resources

An integrated mathematical programming model for a dynamic cellular manufacturing system with limited resources

Virtual Factory: Digital Twin Based Integrated Factory Simulations

The co-evolution problem, which is known as the concurrent evolution of products, processes and production systems, along with increased complexity and shorter manufacturing operation lifecycles, makes modelling, simulation and evaluation of such operations challenging activities for industry players. This paper presents the concept of a digital twin-based virtual factory (VF) and its architecture to support modelling, simulation and evaluation of manufacturing systems while employing multi-user (collaborative and coordinated) virtual reality (VR) learning/training scenarios. This paper also addresses how digital twin-based virtual factory can support factory lifecycle processes by demonstrating the concept in a wind turbine manufacturing plant, including preliminary evaluation by industry experts.

Termeléstervezési módszerek hatékonyságának vizsgálata rugalmas gyártórendszer szimulációs modelljével

A cikk bemutatja egy rugalmas gyártórendszer szimulációs modelljét, amelyben a humán erőforrás kulcsszerepet játszik. A termeléstervezési feladat hatékony megoldására egy speciális keresési módszert javaslunk, amely figyelembe veszi a műszakokban dolgozó emberek valós gyártási képességeit a műszakokra vonatkozó munkák kiosztása során. A feladatot egy másik megközelítéssel is megoldjuk, melynek lényege, hogy a termeléstervezési feladatot visszavezetjük permutációs optimalizálási problémára és a Plant Simulation szoftver genetikus algoritmusával oldjuk meg. A két módszert összehasonlítjuk a vizsgált rugalmas gyártórendszer szimulációs modelljének segítségével és bemutatjuk a futási eredményeket.

On Data Science for Process Systems Modeling, Control and Operations

Data science is emerging as a multidisciplinary field with tremendous recent development in theoretical foundations and expanded applications in both science and engineering. Engineering applications include industrial data analytics, autonomous systems, energy analytics, environmental applications, economic data modeling, image sequence modeling, and other high dimensional time-series data analytics. This paper is concerned with the integration of data science with dynamic systems, monitoring and control. The development of machine learning is reviewed in both a neural-mimic learning route and a learning control route, which deals with intrinsically uncertain dynamic systems. The paper then reviews the interaction of data with process manufacturing systems modeling and control, involving both data and first principles models with varying proportions. Problems include data reconciliation, state and disturbance estimation, system identification, process monitoring, and inferential property estimation. For time series data in process manufacturing systems, we present latent dynamic variable modeling methods to extract the principal dynamics in a low dimensional subspace of the data. The approaches effectively distill latent dynamic features from the data for easy interpretation, prediction, and visualization. Case studies are presented to illustrate how these latent dynamic analytics extract important features for process interpretation, troubleshooting, and monitoring.