Pubblicazioni

Autori:

Gaiardelli, Sebastiano; Lora, Michele; Spellini, Stefano; Fummi, Franco

Titolo:

RRPDG: A Graph Model to Enable AI-Based Production Reconfiguration and Optimization

Anno:

2024

Tipologia prodotto:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Lingua:

Inglese

Formato:

Elettronico

Referee:

Sì

Nome rivista:

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

ISSN Rivista:

1551-3203

Intervallo pagine:

1-11

Parole chiave:

Production; Optimization; Manufacturing; Task analysis; Computational modeling; Technological innovation; Process control; Modeling; smart manufacturing; process control in manufacturing automation

Breve descrizione dei contenuti:

This article introduces the regionalized resource process dependence graphs (RRPDGs): a manufacturing processes representation inspired by the regionalized value state dependence graphs traditionally used in software compilers. An RRPDG is an ordered sequence of nodes, each characterized by stereotyped input and output parameters, encapsulating a transformation of the process state (e.g., a manufacturing operation). RRPDG allow defining complex transformations by composing a set of nodes (i.e., regions), hiding the internal details. Then, RRPDGs are used to automatically reasoning over dynamic reconfiguration and process optimization: an instance of the A-star search algorithm is used to search for possible transformations while pursuing an optimization function. The rules defined in this article over RRPDG models enforce the transformations' correctness. We use RRPDGs to model a real production system while the transformation rules are applied to optimize the system's processes. The proposed representation reduced the search complexity in each experiment, allowing to reach an optimal solution also in the case for which classical approaches were unable to complete before reaching the timeout. In all the experiments, the cost of the solution produced by using the regionalized representation is minor than the the solution produced by using the classical representation.

Pagina Web:

https://ieeexplore.ieee.org/document/10414265

Id prodotto:

137652

Handle IRIS:

11562/1119066

ultima modifica:

28 gennaio 2025

Citazione bibliografica:

Gaiardelli, Sebastiano; Lora, Michele; Spellini, Stefano; Fummi, Franco, RRPDG: A Graph Model to Enable AI-Based Production Reconfiguration and Optimization «IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS» , 2024 , pp. 1-11

Consulta la scheda completa presente nel repository istituzionale della Ricerca di Ateneo