Each week, we select a recently published Open Access article to feature. This week’s article comes from Applied Stochastic Models in Business and Industry  and proposes a procedure for optimizing the payload distribution of freight trains. 

The article’s abstract is given below, with the full article available to read here. 

Berni, R, Cantone, L, Magrini, A, Nikiforova, ND. Hierarchical optimal designs and modeling for engineering: A case-study in the rail sector. Appl Stochastic Models Bus Ind. 2022; 1– 18. doi:10.1002/asmb.2707

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 from the 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.