Published March 5, 2009 | Version v1
Journal article

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

  • 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)

Description

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.

Additional details

Identifiers

Publishing Information

Journal Title
AIP Conference Proceedings
Journal Volume
1107
Journal Issue
1
Journal Page Range
p. 263-268
ISSN
0094-243X
CODEN
APCPCS

Conference

Title
2. Mediterranean conference on intelligent systems and automation
Acronym
CISA'09
Dates
23-25 Mar 2009
Place
Zarzis (Tunisia)

INIS

Country of Publication
United States
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
41039322
Subject category
S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; S42: ENGINEERING;
Resource subtype / Literary indicator
Conference
Descriptors DEI
EVALUATION; MARKOV PROCESS; MATHEMATICAL MODELS; PERFORMANCE; SENSITIVITY ANALYSIS; SIMULATION; STOCHASTIC PROCESSES
Descriptors DEC
STOCHASTIC PROCESSES

Optional Information

Notes
(c) 2009 American Institute of Physics