Use of Simulation Software Environments for the Purpose of Production Optimization

Ojsteršek, Robert; Buchmeister, Borut

doi:10.2507/28th.daaam.proceedings.106

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…Boschert and Rosen (2016) indicate that the digital twin is also connected to existing IT-systems to use the available digital information. Digital twin is used to run simulations and acts as a process-monitoring tool to search for potential incidents, retrieve performance metrics, prevent failure, and then optimize the real system (Ojstersek & Buchmeister, 2017). Those approaches, which combine the real system behaviour with its virtual representation, allows dealing with many optimization problems, such as the deficiency and the lack of control over the data, fragmented between different parts of the system while getting a realistic representation of the system.…”

Section: Digital Twinmentioning

confidence: 99%

Digital twin and virtual reality: a co-simulation environment for design and assessment of industrial workstations

Havard

Jeanne

Lacomblez

et al. 2019

Production & Manufacturing Research

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In the context of the Industry 4.0 and of the digital factory, digital twin and virtual reality represent key technologies to design, simulate and optimize cyber-physical production system and interact with it remotely or in a collaborative way. Moreover, these technologies open up new possibilities which can be involved in the co-design and ergonomics studies of workstations, based on Industry 4.0 components like cobots. In order to satisfy these needs and to create dynamic and immersive virtual environment, it is therefore necessary to combine the capacities of the digital twin to perform simulation of the production system with the capacities of the immersive virtual environment in term of interactions. This paper proposes a co-simulation and communication architecture between digital twin and virtual reality software, then it presents a use case on a human-robot collaborative workplace design and assessment.

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Section: Digital Twinmentioning

confidence: 99%

Digital twin and virtual reality: a co-simulation environment for design and assessment of industrial workstations

Havard

Jeanne

Lacomblez

et al. 2019

Production & Manufacturing Research

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“…Rodič [17] emphasizes the influence of Industry 4.0 on the development of the new simulation methods, which are important for increasing competitiveness. Different simulation methods and simulation software [18] were used in manufacturing systems. Gingu et al [19] used decomposition methods for flow modelling in production systems.…”

Section: Simulation and Production Systemsmentioning

confidence: 99%

Using Maturity Model and Discrete-Event Simulation for Industry 4.0 Implementation

Gajšek¹,

Marolt²,

Rupnik³

et al. 2019

Int. j. simul. model.

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Production environments worldwide transform themselves in order to take the best advantage of the Industry 4.0 guidelines. Automation, data exchange, cyber-physical systems, the IoT, cloud and cognitive computing represent a step in the unknown to these companies, associated with high risks and also the need to restructure their culture. If the execution route is not clearly defined and understandable to all levels of employees, the renovation is too long. The maturity models can be used for the assessment of current Industry 4.0 maturity level, but the practical use of scores and assessed level often requires the involvement of consulting firms. Companies can avoid the involvement of consulting companies with the use of complementary tools. In this paper, we propose a new methodology that combines the Industry 4.0 maturity model and discrete-event simulation tools in the case of steel production company with the possibility of generalization. The combination of these tools in the first step helps the company to assess its current level of maturity for Industry 4.0, and in the second step helps to consider about strengths and weaknesses of possible scenarios for transition to a higher level of maturity.

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“…Because discrete-event simulations do not have to simulate every time slice, they can, typically, run much faster than the corresponding continuous simulation [11]. Practical examples of discrete systems' simulation are presented in solving scheduling problems using linear programming [14], layout and material flow optimization in a digital factory [8], and on production optimization [25]. The mentioned authors present various simulation methods and approaches, and, in doing so, they discuss a problems that arise with the application of simulation methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The simulation model captures all production system real-world characteristics, followed by the optimal solution decision. Depending on the complexity of the production system and the built-in simulation model, we can choose to implement simulation scenarios [25], which allow us detailed simulation modelling according to the previously predicted production system characteristics.…”

Section: Cyber Levelmentioning

confidence: 99%

Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System

Zhang

Buchmeister

Liu

et al. 2019

Springer Series in Advanced Manufacturing

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At the time of Industry 4.0 and the emergence of collaborative workplaces based on the cooperation of robots (machines) and humans, the number of human workplaces in the Industry 4.0 production system is crucial. In this chapter, we present the use of the evolutionary computation methods that use the input data of a real production system and transfer it through the five-stage Cyber-Physical System architecture into the simulation environment in order to determine the optimal number of workers. By using these methods, we confirm the hypothesis of the importance of correctly determining the number of workers in the manufacturing process in Industry 4.0. Number of workers' determination has a key influence on the product flow time, machine utilization and cost-effectiveness of a production system. Research results show the importance and effectiveness of combining evolutionary computation methods and simulation modelling for the purpose of implementing the advanced approaches of Industry 4.0. The demonstrated approach of combining evolutionary computing, simulation environments and methods of Industry 4.0 can be used from mass customization to mass production systems for the purpose of single-criteria or multi-criteria optimization.

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Use of Simulation Software Environments for the Purpose of Production Optimization

Cited by 12 publications

References 18 publications

Digital twin and virtual reality: a co-simulation environment for design and assessment of industrial workstations

Digital twin and virtual reality: a co-simulation environment for design and assessment of industrial workstations

Using Maturity Model and Discrete-Event Simulation for Industry 4.0 Implementation

Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System

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