Modeling Cyclic Interactions within a Production Environment using Transition Adaptive Recurrent Fuzzy Systems

Ståhl, Benjamin; Diepold, Klaus; Pohl, Johannes; Greitemann, Josef; Plehn, Christian; Koch, Jonas; Lohmann, Boris; Reinhart, Gunther

doi:10.3182/20130619-3-ru-3018.00534

Cited by 11 publications

(9 citation statements)

References 13 publications

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“…Following that, fuzzy sets have been investigated as a tool to model those interdependencies on a qualitative basis. Results indicated possible applications for analysis and prediction of influences on manufacturing, but also showed a high demand for pre-set rules about the relation of influences and a lack of quantitative dependencies [29].…”

Section: State Of the Artmentioning

confidence: 98%

Modeling and Analyzing Dynamic Cycle Networks for Manufacturing Planning

et al. 2015

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Knowledge about cyclic influences and their interdependencies supports the prediction of future changes in manufacturing systems and is thus highly valuable for manufacturing planning. Based on system dynamics and former research regarding cycle management, a dynamic cycle network is developed. This enables quantitative analysis and comprehension of cycles within the network, helping to understand future behavior of cyclic influences in manufacturing and to anticipate their potential effects. The results contribute to the development of a planning framework for continuous production planning by further elaborating the innovative concept of cycle management in manufacturing.

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Section: State Of the Artmentioning

confidence: 98%

Modeling and Analyzing Dynamic Cycle Networks for Manufacturing Planning

et al. 2015

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“…Recurrent fuzzy systems extend the classic fuzzy approach of IF...THEN... connection by a time step dependency ( Figure 2). Thus, rules are defined as follows [34][35][36]:…”

Section: Fuzzy Systems Theorymentioning

confidence: 99%

“…and using conventional sums interference and the center of sum defuzzification with singleton support places s x w(j,q) , the output variable x(k + 1) results in (2) [34,36]. The weighting factors g…”

Section: Fuzzy Systemmentioning

confidence: 99%

“…allow for adjustments the system dynamics, but require expert knowledge. Furthermore, the sensitivity of the input variables is increased by the weights [36].…”

Section: Fuzzy Systemmentioning

confidence: 99%

“…Lin et al use design structure matrices to map dependencies of complex fuzzy systems. These n × n-matrices allow for reduction of the required rules [36,37]. In this matrices, existing dependencies are defined by linguistic values L…”

Section: Fuzzy Systemmentioning

confidence: 99%

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Production Strategy Development: Simulation of Dependencies Using Recurrent Fuzzy Systems

Brönner

Wolff

Jovanović

et al. 2020

Systems

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New and unsaturated markets are predominantly located in countries with low incomes. To successfully establish businesses in these new markets, an increasing globalization of value creation is necessary. The integration of these new markets is challenged by their dynamics, turbulence and regulation. Above all, an initially low and volatile demand as well as high barriers for market entry must be counteracted by economical small-series production. As market development is a major challenge for Small and Medium Enterprises (SME), knowledge about the impact of local conditions on their production strategy decisions is essential. Therefore, we design a recurrent fuzzy model combining country- and company-specific input factors as well as internal production parameters to obtain a transparent and reproducible impact statement. The model allows a simplified development of the expert knowledge base by mapping the influences in two steps. The application of the designed model is based on the example of the aCar mobility project, within which an electric vehicle was designed for local value creation in Africa. By applying the model for different African countries, strategy sensitivities can be identified, and recommendations derived.

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