Simulation of matrix-structured manufacturing systems

Schönemann, Malte; Herrmann, Christoph; Greschke, Peter; Thiede, Sebastian

doi:10.1016/j.jmsy.2015.09.002

Cited by 76 publications

(19 citation statements)

References 28 publications

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“…The core idea is to spatially decouple production (industrial robots) and logistics (flow of goods: production parts, tools) and reconnect them using mobile robotics (AGVs) and by networking all production participants. This topic has been taken up in [14], [15] and a comparison of a matrix-structured layout with a classic line production has been carried out. In a simulation approach, the advantages of a high system utilization with simultaneous production of several product types with unknown requirements are shown.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

Matrix Production Systems - Requirements and Influences on Logistics Planning for Decentralized Production Structures

Schmidtke¹,

Rettmann²,

Behrendt³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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In the context of the digital transformation of industry and within the framework of Industrie 4.0 and Factory Planning 4.0, new production-organizational principles with decentralized, modular and freely linked production cells are increasingly being discussed. The principle of matrix production with categorized and standardized workstations offers an extremely versatile production environment. It can be used to meet the challenge of an increasing number of product variants in variable quantities. This concept is predominantly considered from a theoretical point of view. Therefore, many aspects regarding the planning and operation of such systems are still up to debate. With the focus on logistics processes, this paper describes the requirements for such flexible, dynamic routing and self-organizing resources in material supply. Furthermore, they are investigated in a generic, conceptual model for a matrix production. Based on a reference scenario from the automotive industry, classical parameters from logistics and production organization are taken up. The influences with regard to decentralized material supply concepts and structural differences to flow production are shown by the results of simulation experiments with the generic model.

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Section: Literature Review and Related Workmentioning

confidence: 99%

Matrix Production Systems - Requirements and Influences on Logistics Planning for Decentralized Production Structures

Schmidtke¹,

Rettmann²,

Behrendt³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…A use case is applied to validate the simulation approach for the planning of MMS. However, no detailed consideration is given to material supply strategies within the MMS [9].…”

Section: Simulation Of Materials Supply Strategies Within Mmsmentioning

confidence: 99%

“…Filz et al built up on the concept of Schönemann et al [9] and expanded the MMS simulation approach with a focus on material supply strategies. Therefore, different material supply strategies are modelled and analysed with regard to predefined key performance indicators in an agent-based simulation environment [3].…”

Section: Simulation Of Materials Supply Strategies Within Mmsmentioning

confidence: 99%

Simulation-based Data Analysis to Support the Planning of Flexible Manufacturing Systems

Filz¹,

Herrmann²,

Thiede³

2020

SNE

Self Cite

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A matrix-structured manufacturing system represents a flexible manufacturing system that combines volume-and variant-flexible production and strives for efficiency. The system is a modular, cycle-independent manufacturing concept in which all workstations are linked by a flexible transport system. Due to the function of the manufacturing system, the requirements at the individual workstations are not known in advance. Therefore, the planning of the material supply according to requirements is a relevant target value in the planning and design of such a flexible manufacturing system. In order to support the planning, the characteristics of material supply can be investigated with the help of an agentbased simulation. However, the simulation results must be examined in more detail in order to be used for planning flexible manufacturing systems. Therefore, data analysis can be used to derive necessary knowledge from the simulation data. In this context, the aim of this paper is to support the planning of flexible manufacturing systems by developing and validating a simulation-based data analysis framework.

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“…Fig. 1 shows how over time manufacturing has shifted from mass production to mass customization [7]. Fig.…”

Section: A Customization For Smart Manufacturingmentioning

confidence: 99%

“…1. Trend of mass customization vs mass production [7] Optimized decisions and design are facilitated through end-to-end transparency given over the manufacturing process. Moving forward, i4 will bring new ways of creating novel business models and value.…”

Section: A Customization For Smart Manufacturingmentioning

confidence: 99%

Self-organizing tool for smart design with predictive customer needs and wants to realize Industry 4.0

Saldivar

Goh

Chen

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-Following the first three industrial revolutions, Industry 4.0 (i4) aims at realizing mass customization at a mass production cost. Currently, however, there is a lack of smart analytics tools for achieving such a goal. This paper investigates this issues and then develops a predictive analytics framework integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a self-organizing map (SOM) is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The selection of patterns from big data with SOM helps with clustering and with the selection of optimal attributes. A car customization case study shows that the SOM is able to assign new clusters when growing knowledge of customer needs and wants. The self-organizing tool offers a number of features suitable to smart design that is required in realizing Industry 4.0.

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Simulation of matrix-structured manufacturing systems

Cited by 76 publications

References 28 publications

Matrix Production Systems - Requirements and Influences on Logistics Planning for Decentralized Production Structures

Matrix Production Systems - Requirements and Influences on Logistics Planning for Decentralized Production Structures

Simulation-based Data Analysis to Support the Planning of Flexible Manufacturing Systems

Self-organizing tool for smart design with predictive customer needs and wants to realize Industry 4.0

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