Complex engineering and technological processes typically generate high-dimensional data, resulting in a hierarchical structure data with a non-trivial hierarchical structure. To this end, in this article we propose a full procedure for optimizing such processes through optimal experimental designs and modeling. In order to study a hierarchical structure, several types of experimental factors may arise, making the building of the experimental design challenging. Starting from the analysis of a high-dimensional preliminary dataset and a pilot design including nested, branching and shared experimental factors, as well as a new type of experimental factor called composite-form-factor, we build a hierarchical D-optimal experimental design using genetic algorithms to search for the optimal design. We apply our proposal to a real case-study in the rail sector aimed at optimizing the payload distribution of freight trains. In this case-study we also achieve the best train configuration by minimizing the in-train forces. The results are very satisfactory and confirm that our full procedure represents a valid method to be successfully applied for solving similar technological problems.

Hierarchical optimal designs and modeling for engineering: A case-study in the rail sector / Berni, Rossella; Cantone, Luciano; Magrini, Alessandro; Nikiforova, Nedka Dechkova. - In: APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY. - ISSN 1526-4025. - ELETTRONICO. - ..:(2022), pp. 0-0. [10.1002/asmb.2707]

Hierarchical optimal designs and modeling for engineering: A case-study in the rail sector

Berni, Rossella
;
Magrini, Alessandro;Nikiforova, Nedka Dechkova
2022

Abstract

Complex engineering and technological processes typically generate high-dimensional data, resulting in a hierarchical structure data with a non-trivial hierarchical structure. To this end, in this article we propose a full procedure for optimizing such processes through optimal experimental designs and modeling. In order to study a hierarchical structure, several types of experimental factors may arise, making the building of the experimental design challenging. Starting from the analysis of a high-dimensional preliminary dataset and a pilot design including nested, branching and shared experimental factors, as well as a new type of experimental factor called composite-form-factor, we build a hierarchical D-optimal experimental design using genetic algorithms to search for the optimal design. We apply our proposal to a real case-study in the rail sector aimed at optimizing the payload distribution of freight trains. In this case-study we also achieve the best train configuration by minimizing the in-train forces. The results are very satisfactory and confirm that our full procedure represents a valid method to be successfully applied for solving similar technological problems.
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Goal 9: Industry, Innovation, and Infrastructure
Berni, Rossella; Cantone, Luciano; Magrini, Alessandro; Nikiforova, Nedka Dechkova
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2158/1278706
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