Valid Methodology for Using Discrete Event Simulation to Improve the Resource Consumption for the Manufacturing of Masonry Units

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• 作者：Toni Donhauser^a ; ^{toni.donhauser@faps.fau.de" class="auth_mail" title="E-mail the corresponding author} ; Tobias Rackow^a ; Johannes Hirschbrunn^a ; Peter Schuderer^b ; Jö ; rg Franke^a
• 关键词：Discrete event simulation ; Production planning and control ; Complexity management ; Bulk material ; Piece goods ; calcium silicate masonry unit
• 刊名：Procedia CIRP
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：41
• 期：Complete
• 页码：57-62
• 全文大小：154 K

文摘

Owing to a high inflexibility of the factory layout, manufacturers of masonry units are bound to organizational adjustments seizing optimization measures. Regarding such plants, having a given complexity based on a rigid concatenation of heterogeneous sub-processes with heavy goods to be transported, conventional measures such as Lean Management principles involve great efforts in execution. Therefore, an IT solution for planning and controlling the operational processes is to be developed. This solution will be implemented through simulation-supported optimization to support dealing with a higher complexity and setting up a more resource-efficient manufacturing process.

As a basis, a corresponding factory is mapped sufficiently accurate in every detail in a discrete event analysis (DEA) model. In this paper, a methodology, how to configure an arbitrary calcium silicate masonry unit (CS) plant in a simulation model, is presented for the first time. Relevant data is cataloged and modelling approaches for the controlling methods are pointed out.

Special regard is paid to optimization measures at the crucial point of the transition from bulk material to piece goods, which has not been regarded yet in discrete event simulation modelling. The major aspect is a comparison of a unit-based approach and a variable-controlled approach, regarding the runtime.

A case study follows conclusively, which aided in validating the methodology by simulating various scenarios. As a result, several strategic and operational optimization potentials were identified.