Modeling Power of Stochastic Petri Nets for Simulation

1988 ◽  
Vol 2 (4) ◽  
pp. 435-459 ◽  
Author(s):  
Peter J. Haas ◽  
Gerald S. Shedler
Keyword(s):  
Petri Nets ◽  
State Space ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
The State ◽  
Stochastic Petri Nets ◽  
Event Simulation ◽  
Transition Mechanism ◽  
Modeling Power ◽  
Semi Markov Process

Generalized semi-Markov processes and stochastic Petri nets have been proposed as general frameworks for a discrete event simulation on a countable state space. The two formal systems differ, however, with respect to the clock setting (event scheduling) mechanism, the state transition mechanism, and the form of the state space. We obtain conditions under which the marking process of a stochastic Petri net “mimics” a generalized semi-Markov process in the sense that the two processes (and their underlying general state-space Markov chains) have the same finite dimensional distributions. The results imply that stochastic Petri nets have at least the modeling power of generalized semiMarkov processes for discrete event simulation.

Stochastic Petri Nets: Modeling Power and Limit Theorems

1991 ◽  
Vol 5 (4) ◽  
pp. 477-498 ◽  
Author(s):  
Peter J. Haas ◽  
Gerald S. Shedler
Keyword(s):  
Petri Nets ◽  
State Space ◽  
Markov Processes ◽  
Discrete Event ◽  
Building Blocks ◽  
The State ◽  
Stochastic Petri Nets ◽  
Transition Mechanism ◽  
Modeling Power ◽  
Semi Markov Processes

Generalized semi-Markov processes and stochastic Petri nets provide building blocks for specification of discrete event system simulations on a finite or countable state space. The two formal systems differ, however, in the event scheduling (clock-setting) mechanism, the state transition mechanism, and the form of the state space. We have shown previously that stochastic Petri nets have at least the modeling power of generalized semi-Markov processes. In this paper we show that stochastic Petri nets and generalized semi-Markov processes, in fact, have the same modeling power. Combining this result with known results for generalized semi-Markov processes, we also obtain conditions for time-average convergence and convergence in distribution along with a central limit theorem for the marking process of a stochastic Petri net.

scholarly journals On the performance potential of speculative execution based on branch and value prediction

2003 ◽  
Vol 16 (1) ◽  
pp. 83-91
Author(s):  
Pece Mitrevski ◽  
Marjan Gusev
Keyword(s):  
Petri Nets ◽  
Dynamic Behavior ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Stochastic Petri Nets ◽  
Speculative Execution ◽  
Performance Potential ◽  
Value Prediction ◽  
Event Simulation ◽  
Instructions Per Cycle

Fluid Stochastic Petri Nets are used to capture the dynamic behavior of an ILP processor, and discrete-event simulation is applied to assess the performance potential of predictions and speculative execution in boosting the performance of ILP processors that fetch, issue, execute and commit a large number of instructions per cycle.

scholarly journals Modelling and Performance Analysis of Electric Car-Sharing Systems Using Petri Nets

2020 ◽  
Vol 170 ◽  
pp. 03001 ◽  
Author(s):  
A. Hamroun ◽  
K. Labadi ◽  
M. Lazri
Keyword(s):  
Petri Nets ◽  
Simulation Analysis ◽  
Discrete Event ◽  
Stochastic Petri Nets ◽  
Car Sharing ◽  
Event Simulation ◽  
Electric Car ◽  
Bike Sharing ◽  
And Performance ◽  
Charging Stations

Car sharing systems emerged as a new answer to mobility challenges in smart and sustainable cities. Despite their apparent success, design and exploitation of such systems raise crucial strategic and operational challenges. To help planners and decision makers, simulation, analysis and optimization models are unavoidable. Based on the formal modelling and analysis power of stochastic Petri nets, this paper proposes a discrete event simulation model for electric car sharing systems for performance and analysis purposes, taking into account their complex dynamic behaviour, organization and parameters including capacities of the stations, battery and energy availability, locations of charging stations and also their car maintenance activities, not negligible compared to the case of bike-sharing systems.

scholarly journals A Methodology to Adapt and Understand a Manufacturing System and Operations using Discrete-Event Simulatio

2019 ◽  
Vol 8 (6) ◽  
pp. 4998-5003
Keyword(s):  
Discrete Event Simulation ◽  
Manufacturing Industry ◽  
Manufacturing System ◽  
Discrete Event ◽  
The State ◽  
State Variables ◽  
Event Simulation ◽  
Potential Benefits ◽  
Short Hand

Discrete-Event Simulation (DES) is concerned with system and modeling of that system, where the state of the system is transformed at different discrete points from time to time, and several event occurs from time to time and the changes in state variables will transform then activities/attributes connected to these state variables changes according to the event. It is a robust methodology in the manufacturing industry for strategic, tactical, and operational applications for an organization, and yet organizations ignore to use simulation and do not rely on it. Moreover, companies that are using DES are not using the potential benefits but merely used as a short-hand basis for problems like bottlenecks, optimization, and in later stages of production like PLM, this paper aims to apply and analyze Discrete-Event Simulation through a Manufacturing System. The work describes here is to understand the concept of simulation for a system and to practice Discrete Event methodology

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