skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: 'PSA-SPN' - A Parameter Sensitivity Analysis Method Using Stochastic Petri Nets: Application to a Production Line System

Abstract

The dynamic behavior of a discrete event dynamic system can be significantly affected for some uncertain changes in its decision parameters. So, parameter sensitivity analysis would be a useful way in studying the effects of these changes on the system performance. In the past, the sensitivity analysis approaches are frequently based on simulation models. In recent years, formal methods based on stochastic process including Markov process are proposed in the literature. In this paper, we are interested in the parameter sensitivity analysis of discrete event dynamic systems by using stochastic Petri nets models as a tool for modelling and performance evaluation. A sensitivity analysis approach based on stochastic Petri nets, called PSA-SPN method, will be proposed with an application to a production line system.

Authors:
 [1];  [2];  [3]
  1. EPMI-ECS, 13 Bld de l'Hautil, 95092 Cergy-Pontoise Cedex (France)
  2. INRIA Rennes, DIONYSOS, Bretagne Atlantique, Campus Universitaire de Beaulieu, 35042 Rennes (France)
  3. UTT-ICD, Universite de Technologie de Troyes, 12 rue Marie Curie, BP2060, 10010 Troyes (France)
Publication Date:
OSTI Identifier:
21293327
Resource Type:
Journal Article
Journal Name:
AIP Conference Proceedings
Additional Journal Information:
Journal Volume: 1107; Journal Issue: 1; Conference: CISA'09: 2. Mediterranean conference on intelligent systems and automation, Zarzis (Tunisia), 23-25 Mar 2009; Other Information: DOI: 10.1063/1.3106483; (c) 2009 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-243X
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; EVALUATION; MARKOV PROCESS; MATHEMATICAL MODELS; PERFORMANCE; SENSITIVITY ANALYSIS; SIMULATION; STOCHASTIC PROCESSES

Citation Formats

Labadi, Karim, Saggadi, Samira, and Amodeo, Lionel. 'PSA-SPN' - A Parameter Sensitivity Analysis Method Using Stochastic Petri Nets: Application to a Production Line System. United States: N. p., 2009. Web. doi:10.1063/1.3106483.
Labadi, Karim, Saggadi, Samira, & Amodeo, Lionel. 'PSA-SPN' - A Parameter Sensitivity Analysis Method Using Stochastic Petri Nets: Application to a Production Line System. United States. https://doi.org/10.1063/1.3106483
Labadi, Karim, Saggadi, Samira, and Amodeo, Lionel. 2009. "'PSA-SPN' - A Parameter Sensitivity Analysis Method Using Stochastic Petri Nets: Application to a Production Line System". United States. https://doi.org/10.1063/1.3106483.
@article{osti_21293327,
title = {'PSA-SPN' - A Parameter Sensitivity Analysis Method Using Stochastic Petri Nets: Application to a Production Line System},
author = {Labadi, Karim and Saggadi, Samira and Amodeo, Lionel},
abstractNote = {The dynamic behavior of a discrete event dynamic system can be significantly affected for some uncertain changes in its decision parameters. So, parameter sensitivity analysis would be a useful way in studying the effects of these changes on the system performance. In the past, the sensitivity analysis approaches are frequently based on simulation models. In recent years, formal methods based on stochastic process including Markov process are proposed in the literature. In this paper, we are interested in the parameter sensitivity analysis of discrete event dynamic systems by using stochastic Petri nets models as a tool for modelling and performance evaluation. A sensitivity analysis approach based on stochastic Petri nets, called PSA-SPN method, will be proposed with an application to a production line system.},
doi = {10.1063/1.3106483},
url = {https://www.osti.gov/biblio/21293327}, journal = {AIP Conference Proceedings},
issn = {0094-243X},
number = 1,
volume = 1107,
place = {United States},
year = {Thu Mar 05 00:00:00 EST 2009},
month = {Thu Mar 05 00:00:00 EST 2009}
}