Stochastic cyclic flow lines: non-blocking,Markovian models

In this paper we consider stochastic cyclic flow lines where identical sets of jobs are repeatedly produced in the same loading and processing sequence. Each machine has an input buffer with enough capacity. Processing times are stochastic. We model the shop as a stochastic event graph, a class of Petri nets. We characterise the ergodicity condition and the cycle time. For the case where processing times are exponentially distributed, we present a way of computing queue length distributions. For two-machine cases, by the matrix geometric method, we compute the exact queue length distributions. For general cases, we present two methods for approximately decomposing the line model into two-machine submodels, one based on starvation propagation and the other based on transition enabling probability propagation. We experiment our approximate methods for various stochastic cyclic flow lines and discuss performance characteristics as well as accuracy of the approximate methods. Finally, we discuss the effects of job processing sequences of stochastic cyclic flow lines.     

About the stochastic and continuous Petri nets equivalence in the long run

Reliability analysis is often based on stochastic discrete event models like stochastic Petri nets (SPNs). For large dynamical systems with numerous components, the analytical expression of the SPNs steady state is full of complexities because of the combinatory explosion with discrete models. Moreover, the estimation of mean markings thanks to simulations is time consuming in case of rare events. For these reasons, Petri net fluidification may be an interesting alternative to provide a reasonable estimate of the asymptotic behavior of stochastic processes. Unfortunately, the steady states of SPNs and timed continuous Petri nets (contPNs) with the same structure, same initial marking and same firing rates are mainly often different. The region of SPN steady states (when firing rates are defined in a polyhedral area) contrasts with that of contPN ones. The purpose of this paper is to illuminate this issue in taking advantage of the piecewise-affine hybrid structure of contPNs. Regions and critical regions are defined in the marking space in order to characterize this structure. Based on this characterization, the main contribution is to propose a transformation of the considered SPN into a contPN with the same structure, modified firing rates and homothetic initial marking so that the corrected contPN converges partially to the same mean marking than the SPN. Consequently, a global understanding of an SPN steady state can be obtained according to the corrected contPN.     

SimHPN: A MATLAB toolbox for simulation, analysis and design with hybrid Petri nets

This paper presents a MATLAB embedded package for hybrid Petri nets called SimHPN. It offers a collection of tools devoted to simulation, analysis and synthesis of dynamical systems modeled by hybrid Petri nets. The package supports several server semantics for the firing of both, discrete and continuous, types of transitions. Besides providing different simulation options, SimHPN offers the possibility of computing steady state throughput bounds for continuous nets. For such a class of nets, optimal control and observability algorithms are also implemented. The package is fully integrated in MATLAB which allows the creation of powerful algebraic, statistical and graphical instruments that exploit the routines available in MATLAB.     

Diffusion approximation for signaling stochastic networks

This paper introduces an unified approach to diffusion approximations of signaling networks. This is accomplished by the characterization of a broad class of networks that can be described by a set of quantities which suffer exchanges stochastically in time. We call this class stochastic Petri nets with probabilistic transitions, since it is described as a stochastic Petri net but allows a finite set of random outcomes for each transition. This extension permits effects on the network which are commonly interpreted as “routing” in queueing systems. The class is general enough to include, for instance, G-networks with negative customers and triggers as a particular case. With this class at hand, we derive a heavy traffic approximation, where the processes that drive the transitions are given by state-dependent Poisson-type processes and where the probabilities of the random outcomes are also state-dependent. The objective of this approach is to have a diffusion approximation which can be readily applied in several practical problems. We illustrate the use of the results with some numerical experiments.     

Nondeterministic dynamic programming on a parallel coprocessing system

We describe how nondeterministic dynamic programming (DP) algorithms can be designed for a new class of parallel coprocessing systems using “functional memory”, an architecture based upon dataflow computer principles. We also show how Petri nets can be used to model and express such parallel DP algorithms. Finally, we discuss architectural improvements that would facilitate the processing of Petri net models of nondeterministic DP algorithms on functional memory computers (FMC).     

An upper bound on the cycle time of a stochastic marked graph using incomplete information on the transition firing time distributions

Stochastic marked graphs, a special class of stochastic timed Petri nets, are used for modelling and analyzing decision-free dynamic systems with uncertainties in timing. The model allows evaluating the performance of such systems under a cyclic process. Given the probabilistic characteristics of the transition times, the cycle time of the system can be determined from the initial marking. In this contribution, we compute an upper bound on the cycle time of a stochastic marked graph in case the probabilistic characteristics of the transition times are not fully specified.     

Numerical investigation of finite-source multiserver systems with different vacation policies

Systems with vacations are usually modeled and analyzed by queueing theory, and almost all works assume that the customer source is infinite and the arrival process is Poisson. This paper aims to present an approach for modeling and analyzing finite-source multiserver systems with single and multiple vacations of servers or all stations, using the Generalized Stochastic Petri nets model. We show how this high level formalism, allows a simple construction of detailed and compact models for such systems and to obtain easily the underlying Markov chains. However, for real vacation systems, the models may have a huge state space. To overcome this problem, we give the algorithms for automatically computing the infinitesimal generator, for the different vacation policies. In addition, we develop the formulas of the main exact stationary performance indices. Through numerical examples, we discuss the effect of server number, vacation rate and vacation policy on the system’s performances.     

Modeling and analysis using hybrid Petri nets

This paper is devoted to the use of hybrid Petri nets (PNs) for modeling and control of hybrid dynamic systems (HDS). Modeling, analysis and control of HDS attract ever more of researchers’ attention and several works have been devoted to these topics. We consider in this paper the extensions of the PN formalism (initially conceived for modeling and analysis of discrete event systems) in the direction of hybrid modeling. We present, first, the continuous PN models. These models are obtained from discrete PNs by the fluidification of the markings. They constitute the first steps in the extension of PNs toward hybrid modeling. Then, we present two hybrid PN models, which differ in the class of HDS they can deal with. The first one is used for deterministic HDS modeling, whereas the second one can deal with HDS with nondeterministic behavior.     

Encoding the dynamics of deterministic systems

We present a model for the dynamics of discrete deterministic systems, based on an extension of the Petri nets framework. Our model relies on the definition of a priority relation between conflicting transitions, which is encoded by orienting the edges of a transition conflict graph. We provide a characterization in terms of a local consistency condition of those deterministic systems whose dynamic behavior can be encoded using our approach. Finally, we consider the problem of recognizing when an orientation of the transition conflict graph is valid for encoding the dynamic behavior of a system.     

Multilevel hybrid split-step implicit tau-leap

In biochemically reactive systems with small copy numbers of one or more reactant molecules, the dynamics is dominated by stochastic effects. To approximate those systems, discrete state-space and stochastic simulation approaches have been shown to be more relevant than continuous state-space and deterministic ones. In systems characterized by having simultaneously fast and slow timescales, existing discrete space-state stochastic path simulation methods, such as the stochastic simulation algorithm (SSA) and the explicit tau-leap (explicit-TL) method, can be very slow. Implicit approximations have been developed to improve numerical stability and provide efficient simulation algorithms for those systems. Here, we propose an efficient Multilevel Monte Carlo (MLMC) method in the spirit of the work by Anderson and Higham (SIAM Multiscal Model. Simul. 10(1), 2012). This method uses split-step implicit tau-leap (SSI-TL) at levels where the explicit-TL method is not applicable due to numerical stability issues. We present numerical examples that illustrate the performance of the proposed method.     

Numerical computation of response time distributions using stochastic reward nets

We consider the numerical computation of response time distributions for closed product form queueing networks using thetagged customer approach. We map this problem on to the computation of the time to absorption distribution of a finite-state continuous time Markov chain. The construction and solution of these Markov chains is carried out using a variation of stochastic Petri nets called stochastic reward nets (SRNs). We examine the effects of changing the service discipline and the service time distribution at a queueing center on the response time distribution. A multiserver queueing network example is also presented. While the tagged customer approach for computing the response time distribution is not new, this paper presents a new approach for computing the response time distributions using SRNs.This research was sponsored in part by the National Science Foundation under Grant CCR-9108114 and by the Naval Surface Warfare Center under contract N60921-92-C-0161.     

Expansions for Joint Laplace Transform of Stationary Waiting Times in (max,+)-linear Systems with Poisson Input

We give a Taylor series expansion for the joint Laplace transform of stationary waiting times in open (max,+)-linear stochastic systems with Poisson input. Probabilistic expressions are derived for coefficients of all orders. Even though the computation of these coefficients can be hard for certain systems, it is sufficient to compute only a few coefficients to obtain good approximations (especially under the assumption of light traffic). Combining this new result with the earlier expansion formula for the mean stationary waiting times, we also provide a Taylor series expansion for the covariance of stationary waiting times in such systems.It is well known that (max,+)-linear systems can be used to represent stochastic Petri nets belonging to the class of event graphs. This class contains various instances of queueing networks like acyclic or cyclic fork-and-join queueing networks, finite or infinite capacity tandem queueing networks with various types of blocking, synchronized queueing networks and so on. It also contains some basic manufacturing models such as kanban networks, assembly systems and so forth. The applicability of this expansion technique is discussed for several systems of this type.     

Asymptotic variability analysis for multi-server generalized Jackson network in overloaded

The asymptotic variability analysis is studied for multi-server generalized Jackson network. It is characterized by law of the iterated logarithm (LIL), which quantifies the magnitude of asymptotic stochastic fluctuations of the stochastic processes compensated by their deterministic fluid limits. In the overloaded (OL) case, the asymptotic variability is studied for five performance measures: queue length, workload, busy time, idle time and number of departures. The proof is based on strong approximations, which approximate discrete performance processes with (reflected) Brownian motions. We conduct numerical examples to provide insights on these LIL results.     

Laplace Transform and Moments of Waiting Times in Poisson Driven (max,+) Linear Systems

(Max,+) linear systems can be used to represent stochastic Petri nets belonging to the class of event graphs. This class contains various instances of queueing networks like acyclic or cyclic fork-and-join queueing networks, finite or infinite capacity tandem queueing networks with various types of blocking, synchronized queueing networks and so on. It also contains some basic manufacturing models such as kanban networks, assembly systems and so forth.In their 1997 paper, Baccelli, Hasenfuss and Schmidt provide explicit expressions for the expected value of the waiting time of the nth customer in a given subarea of a (max,+) linear system. Using similar analysis, we present explicit expressions for the moments and the Laplace transform of transient waiting times in Poisson driven (max,+) linear systems. Furthermore, starting with these closed form expressions, we also derive explicit expressions for the moments and the Laplace transform of stationary waiting times in a class of (max,+) linear systems with deterministic service times. Examples pertaining to queueing theory are given to illustrate the results.     

A Petri net approach to the modelling and analysis of flexible manufacturing systems

In this paper we present an approach for modelling and analyzing flexible manufacturing systems (FMSs) using Petri nets. In this approach, we first build a Petri net model (PNM) of the given FMS in a bottom-up fashion and then analyze important qualitative aspects of FMS behaviour such as existence/absence of deadlocks and buffer overflows. The basis for our approach is a theorem we state and prove for computing the invariants of the union of a finite number of Petri nets when the invariants of the individual nets are known. We illustrate our approach using two typical manufacturing systems: an automated transfer line and a simple FMS.A shorter version of this paper was presented at the 1st ORSA/TIMS Special Interest Conference on FMSs, University of Michigan, Ann Arbor, August 1984.     

单自由度摩擦系统离散模型

发展了两种随机离散数学模型:导出了一个以二维平均映射描述的随机模型,并建立了一个概率预报模型.通过实例对不同模型进行了比较,对于平均映射模型,分岔图指出了外噪声对系统性质的影响,通过符号动力学方法分析指出概率预报模型的随机性质.     

Switching diffusion logistic models involving singularly perturbed Markov chains: Weak convergence and stochastic permanence

Focusing on stochastic dynamics involve continuous states as well as discrete events, this article investigates stochastic logistic model with regime switching modulated by a singular Markov chain involving a small parameter. This Markov chain undergoes weak and strong interactions, where the small parameter is used to reflect rapid rate of regime switching among each state class. Two-time-scale formulation is used to reduce the complexity. We obtain weak convergence of the underlying system so that the limit has much simpler structure. Then we utilize the structure of limit system as a bridge, to invest stochastic permanence of original system driving by a singular Markov chain with a large number of states. Sufficient conditions for stochastic permanence are obtained. A couple of examples and numerical simulations are given to illustrate our results.