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The integration of genetic algorithms to optimize the networks of value chains could enormously improve the performance of supply chains. For this reason, this paper describes in more detail the application of genetic algorithms in the value chains of the automotive industry. For this purpose, a theoretical model is built up to evaluate whether the application of the model can optimize the value chain. This option is described, analyzed and its restrictions are shown. Instead of looking at the entire network, individual finished goods and their bill of material are used as a basis for optimization, which greatly reduces the complexity of the original problem. The original complexity of the supply chain networks can thus be reduced and considered based on the bill of material.
Fuzzy system based on two-step cascade genetic optimization strategy for tobacco tar prediction
(2019)
There are many challenges in accurately measuring cigarette tar constituents. These include the need for standardized smoke generation methods related to unstable mixtures. In this research were developed algorithms using fusion of artificial intelligence methods to predict tar concentration. Outputs of development are three fuzzy structures optimized with genetic algorithms resulting in genetic algorithm (GA)-FUZZY, GA-adaptive neuro fuzzy inference system (ANFIS), GA-GA-FUZZY algorithms. Proposed algorithms are used for the tar prediction in the cigarette production process. The results of prediction are compared with gas chromatograph (high-performance liquid chromatography (HPLC)) readings.