Complex engineering and technological processes typically generate 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 fromthe analysis of a 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. 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.

Berni, R., Cantone, L., Magrini, A., Nikiforova, N.d. (2022). Hierarchical optimal designs and modeling for engineering: A case‐study in the rail sector. APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, 1-18 [10.1002/asmb.2707].

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

Cantone, Luciano;
2022

Abstract

Complex engineering and technological processes typically generate 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 fromthe analysis of a 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. 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.
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-IND/14
English
D-optimality criterion, genetic algorithm, mixed linear model, random effects
https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.2707
Berni, R., Cantone, L., Magrini, A., Nikiforova, N.d. (2022). Hierarchical optimal designs and modeling for engineering: A case‐study in the rail sector. APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, 1-18 [10.1002/asmb.2707].
Berni, R; Cantone, L; Magrini, A; Nikiforova, Nd
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2108/304794
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