Design and maintenance of reliable manufacturing systems calls for the development of formal models that allow for performance analysis. We consider the class of manufacturing systems such that the production of a workpiece consists of a sequence of manufacturing stages performed by fault-prone, repairable, workstations, equipped with finite-sized input buffers. We name this kind of systems production lines. Relying on an expressive property specification formalism, namely the hybrid automata specification language, we introduce a framework that allows for 1) the automatic generation of stochastic Petri nets models of arbitrary sized production lines and 2) the generation of a number of sophisticated performance indicators (in terms of hybrid automata) for analysing the dynamics of a production line. We validate our approach by presenting a number of experiments executed by means of the statistical model checker Cosmos.

Performance Analysis of Production Lines Through Statistical Model Checking

Ballarini P.;Horvath A.
2021-01-01

Abstract

Design and maintenance of reliable manufacturing systems calls for the development of formal models that allow for performance analysis. We consider the class of manufacturing systems such that the production of a workpiece consists of a sequence of manufacturing stages performed by fault-prone, repairable, workstations, equipped with finite-sized input buffers. We name this kind of systems production lines. Relying on an expressive property specification formalism, namely the hybrid automata specification language, we introduce a framework that allows for 1) the automatic generation of stochastic Petri nets models of arbitrary sized production lines and 2) the generation of a number of sophisticated performance indicators (in terms of hybrid automata) for analysing the dynamics of a production line. We validate our approach by presenting a number of experiments executed by means of the statistical model checker Cosmos.
2021
17th European Performance Engineering Workshop, EPEW 2021, and the 26th International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2021
online
2021
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Springer Science and Business Media Deutschland GmbH
13104
264
281
978-3-030-91824-8
978-3-030-91825-5
Hybrid automata specifications; Manufacturing systems; Statistical model checking
Ballarini P.; Horvath A.
File in questo prodotto:
File Dimensione Formato  
paper.pdf

Accesso riservato

Tipo di file: POSTPRINT (VERSIONE FINALE DELL’AUTORE)
Dimensione 1.26 MB
Formato Adobe PDF
1.26 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1846391
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact