Generic data structure and validation methodology for simulation of manufacturing systems

Popovics, Gergely; Pfeiffer, András; Monostori, László

doi:10.1080/0951192x.2016.1187296

Cited by 20 publications

(13 citation statements)

References 11 publications

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“…At this stage, real data from several sources is integrated and stored in the BDW and can be provided to the simulation model that has also been validated. See [42], [43] for references to simulation model validation techniques.…”

Section: Proposed Approachmentioning

confidence: 99%

On the use of simulation as a Big Data semantic validator for supply chain management

Vieira

Dias

Santos

et al. 2020

Simulation Modelling Practice and Theory

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Simulation stands out as an appropriate method for the Supply Chain Management (SCM) field. Nevertheless, to produce accurate simulations of Supply Chains (SCs), several business processes must be considered. Thus, when using real data in these simulation models, Big Data concepts and technologies become necessary, as the involved data sources generate data at increasing volume, velocity and variety, in what is known as a Big Data context. While developing such solution, several data issues were found, with simulation proving to be more efficient than traditional data profiling techniques in identifying them. Thus, this paper proposes the use of simulation as a semantic validator of the data, proposed a classification for such issues and quantified their impact in the volume of data used in the final achieved solution. This paper concluded that, while SC simulations using Big Data concepts and technologies are within the grasp of organizations, their data models still require considerable improvements, in order to produce perfect mimics of their SCs. In fact, it was also found that simulation can help in identifying and bypassing some of these issues.

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Section: Proposed Approachmentioning

confidence: 99%

On the use of simulation as a Big Data semantic validator for supply chain management

Vieira

Dias

Santos

et al. 2020

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

show abstract

“…Discrete-event-simulation (DES) is mostly used to solve MS problems. Numerous benefits to use DES defined by [32]include: simulate a real system in a short time, study the interaction between different components, reduce cost and risk, monitor and control the system and others.…”

Section: Figure 6 Evolution Of Simulation Applicationmentioning

confidence: 99%

Improvement opportunities of a Simulation/Expert System Approach for Manufacturing System Sizing: A review and proposal

Imen¹,

Masmoudi²,

Elleuch³

et al. 2019

Adv. sci. technol. eng. syst. j.

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Manufacturing System (MS) sizing is a crucial task to complete in order to obtain the desired MS performance and efficiency. It involves selecting the required number of resources from each used type in a given planning horizon. In fact, different approaches coupling simulation/optimization tools have been developed to solve this issue and evaluate the MS performance. One of these approaches is the Simulation Expert System Approach (SESA). Unfortunately, the application domain of this approach is limited in sizing only the production resources (machines and labor) but neglects the material handling system (MHS) components. Moreover, omitting the transferring problem is not viable in the real world due to its importance in each shop floor. Thus, the aim of this paper is to describe the evolution of SESA, then, to check if the simulation optimization tools used in SESA are still relevant. This paper also investigates the importance of incorporating MHS in this approach and finally proposes some improvement opportunities for SESA including the tackling of the MHS fleet sizing problem. In fact, the wide literature review performed in this research indicates that SESA is still a pertinent approach but it must be improved. Therefore, it is expected that SESA improvement opportunities proposed in this work will greatly assist industrialists in enhancing the overall MS performance, providing a significant productivity increase and a minimization of the total production costs.

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“…Among these models, ISA-95 is highly recognized in the industry due to its focus on the integration between these two types of solutions (ERP and MES) and its high level of application in the industrial environment [63,66,67]. ISA-95, also known as IEC 62264, provides consistent terminologies, models and interfaces for integrating data exchange between different levels of a production system's hierarchy [67,68]. The standard defines the MES data structure and its services associated with manufacturing operations such as (i) product definition; (ii) production planning; (iii) production capacity management and (iv) production performance evaluation.…”

Section: Industrial Standards For Data Exchangementioning

confidence: 99%

Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

et al. 2018

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Technological developments along with the emergence of Industry 4.0 allow for new approaches to solve industrial problems, such as the Job-shop Scheduling Problem (JSP). In this sense, embedding Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) is a highly promising approach to handle complex and dynamic JSPs. This paper proposes a data exchange framework in order to deal with the JSP considering the state-of-the-art technology regarding MAS, CPS and industrial standards. The proposed framework has self-configuring features to deal with disturbances in the production line. This is possible through the development of an intelligent system based on the use of agents and the Internet of Things (IoT) to achieve real-time data exchange and decision making in the job-shop. The performance of the proposed framework is tested in a simulation study based on a real industrial case. The results substantiate gains in flexibility, scalability and efficiency through the data exchange between factory layers. Finally, the paper presents insights regarding industrial applications in the Industry 4.0 era in general and in particular with regard to the framework implementation in the analyzed industrial case.

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Generic data structure and validation methodology for simulation of manufacturing systems

Cited by 20 publications

References 11 publications

On the use of simulation as a Big Data semantic validator for supply chain management

On the use of simulation as a Big Data semantic validator for supply chain management

Improvement opportunities of a Simulation/Expert System Approach for Manufacturing System Sizing: A review and proposal

Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

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