IPA for continuous stochastic marked graphs

This paper presents a unified framework for the Infinitesimal Perturbation Analysis (IPA) gradient-estimation technique in the setting of marked graphs. It proposes a systematic approach for computing the derivatives of sample performance functions with respect to structural and control parameters....

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Bibliographic Details
Published in:Automatica (Oxford) 2013-05, Vol.49 (5), p.1204-1215
Main Authors: Wardi, Y., Giua, A., Seatzu, C.
Format: Article
Language:English
Subjects:
Applied sciences
Automatic Control Engineering
Computer Science
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Continuous Petri nets
Exact sciences and technology
Flows in networks. Combinatorial problems
Infinitesimal perturbation analysis
Information retrieval. Graph
Marked graphs
Operational research and scientific management
Operational research. Management science
Simulation
Software
Stochastic hybrid systems
Theoretical computing
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Summary:This paper presents a unified framework for the Infinitesimal Perturbation Analysis (IPA) gradient-estimation technique in the setting of marked graphs. It proposes a systematic approach for computing the derivatives of sample performance functions with respect to structural and control parameters. The resulting algorithms are recursive in both time and network flows, and their successive steps are computed in response to the occurrence and propagation of certain events in the network. Such events correspond to discontinuities in the network flow-rates, and their special characteristics are due to the properties of continuous transitions and fluid places. Following a general outline of the framework we focus on a simple yet canonical example, and investigate throughput and workload-related performance criteria as functions of structural and control variables. Simulation experiments support the analysis and testify to the potential viability of the proposed approach.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2013.02.006