The aim of this paper is to increase the practitioners' and researchers' familiarity on modeling, simulation and intelligent algorithms for solving machine loading (ML) problem. To achieve this goal, this paper presents the results of a systematic literature review carried out on 54 scientific articles which have dealt with topic. The relating data was collected from the Scopus database; then, Microsoft Excel™ was used for descriptive analysis. The results of this review give some key learning of the trends of the use of modeling, simulation and intelligent algorithm for solving the ML problem and also provide a background for future research related to the field.
Modeling, simulation and intelligent algorithms for solving the machine-loading problem: a literature review / Casella, G.; Bottani, E.; Murino, T.; Solari, F.. - 2023:(2023). (Intervento presentato al convegno 22nd International Conference on Modeling and Applied Simulation, MAS 2023 tenutosi a Athens nel 18-20 September 2023) [10.46354/i3m.2023.mas.013].
Modeling, simulation and intelligent algorithms for solving the machine-loading problem: a literature review
Casella G.;Bottani E.
;Solari F.
2023-01-01
Abstract
The aim of this paper is to increase the practitioners' and researchers' familiarity on modeling, simulation and intelligent algorithms for solving machine loading (ML) problem. To achieve this goal, this paper presents the results of a systematic literature review carried out on 54 scientific articles which have dealt with topic. The relating data was collected from the Scopus database; then, Microsoft Excel™ was used for descriptive analysis. The results of this review give some key learning of the trends of the use of modeling, simulation and intelligent algorithm for solving the ML problem and also provide a background for future research related to the field.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
