Christos Cassandras. Discrete Event Systems: Modeling and Performance Analysis.Irwinand Aksen, Boston, Massachusetts, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....models are increasingly used in the performance analysis of communications [3, 10, 13] and manufacturing systems. On the other hand, stochastic Petri nets with discrete places provide a useful framework for specifying and solving performance and reliability models of discrete event dynamic systems [1, 6, 9, 17, 19]. It is natural to extend the stochastic Petri net framework to Fluid Stochastic Petri Nets (FSPNs) by introducing places with continuous tokens and arcs with fluid flow so as to handle stochastic fluid flow systems. This paper extends the model in an earlier paper [18] by allowing the level of ....

C. G. Cassandras, Discrete Event Systems: Modeling and Performance Analysis, Aksen Associates, Holmwood, IL, 1993.

....primitives that permit unbuffered transfer and synchronization without data. 3.1.4 DISCRETE EVENT A Discrete Event (DE) system can be defined as a discrete state event driven system. In other words, its state evolution depends entirely on the occurrence of asynchronous discrete events over time [Cas93]. An event is an instantaneous action that causes transitions from one discrete state to another. The interaction between computational tasks is accomplished by signals. In the discrete event model, a signal is a set of atomic events that occur in some instant of physical time. Thus, each event ....

C. G. Cassandras, Discrete Event Systems: Modeling and Performance Analysis. Boston, MA: Aksen Associates, 1993.

....engineers rely on simulation languages. While these often compromise the formal nature of formalisms, they o#er in return compact representations of complex behavior. The class of simulation languages has been formally characterized using a formalism called generalized semi Markov process, or GSMP [7, 8]. The particular aspect of simultaneity, sometimes called concurrency of discrete events, that we are referring to is that of two or more discrete events that occur at the same time. Here, an event can be thought of as a change in system state at a discrete point in time. One often neglected ....

....algorithm for IFR models. 27 4 Application to GSMPs A GSMP is a formalism used to describe a class of simulation languages. There have been a number of attempts to formally construct a GSMP formalism, and the results are several very closely related but slightly di#erent constructs [8, 7]. GSMPs use di#erent terminology, and to remain consistent, we take some liberty and continue use SPN terminology when applied to GSMPs. For those familiar with GSMP terminology, we use transition instead of event, enabled instead of active, and a transition fires instead of an event ....

C. G. Cassandras, Discrete Event Systems: Modeling and Performance Analysis. Homewood, Illinois: Aksen Associates Incorporated Publishers, 1993.

.... M OST man made systems are discrete event systems (DESs) owing to the manner in which they evolve: In response to events that are spontaneous, instantaneous, asynchronous (thus discrete in nature) Ramadge and Wonham [9] introduced the theory of supervisory control of discrete event systems [8] [3], where they employed an automaton based model of the system, called a plant, and studied how another automaton, called a supervisor, can be employed to restrict its behavior. The control speci cations which express the constrains that one wishes to impose on the system s behavior are modeled as ....

C. G. Cassandras. Discrete Event Systems: Modeling and Performance Analysis. Aksen Associates, Boston, MA, 1993.

..... A discrete event simulator is usually implemented with a global event queue that automatically sorts the events. Discrete event models may have causality problems due to zero delay in feedback loops, which are discussed in Section 2.2. A good overview on discrete event system is given in [23]. Synchronous Model A synchronous model is a special case of a discrete event system. In a synchronous model all signals have the same set of tags. Tags do not include explicit time information, but are only used to give an order of the events. The synchronous assumption can be formulated ....

C. G. Cassandras. Discrete Event Systems: Modeling and Performance Analysis. Asken Associates, 1993.

....an electronic equivalent to a good old fashioned lab experiment and has been used for decades for various research and engineering areas. Simulation is a process through which a system model is evaluated numerically, and the data from this process is used to estimate various quantities of interest [1]. Although simulation is not the exact real thing , it is the next best thing we have to actually building some very expensive and complicated systems in order to just experiment with them [1] Application examples using simulation technology include manufacturing system design, communication ....

.... evaluated numerically, and the data from this process is used to estimate various quantities of interest [1] Although simulation is not the exact real thing , it is the next best thing we have to actually building some very expensive and complicated systems in order to just experiment with them [1]. Application examples using simulation technology include manufacturing system design, communication networks design and various protocol testings for handling messages contending for network resources, and so on. In communication network research, different areas are being extensively studied, ....

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Christos G. Cassandras, Discrete Event Systems: Modeling and Performance Analysis, Aksen Associates Incorporated Publishers, 1993.

....a formidable task. Because of a mixed, discrete and continuous state space, simulative solution also poses some interesting challenges, which are addressed in the paper. 1 Introduction Stochastic Petri nets provide a convenient and concise method of describing discrete event dynamic systems [1, 4, 6, 12, 15]. One of the diculties encountered while using stochastic Petri nets is that the underlying reachability graph tends to be very large in practical problems. Drawing a parallel with uid ow approximations in performance analysis of queueing systems [3, 7, 11] SPNs have been extended to include ....

C. G. Cassandras, Discrete Event Systems: Modeling and Performance Analysis, Aksen Associates, Holmwood, IL, 1993.

....when the model reaches some particular state or states, and the action is enabled. When reactivation occurs, the action must start over and choose a new completion time. This is the same thing as prd when an action becomes disabled, and is essentially the equivalent of a reset button. GSMPs [10, 5] are commonly used to describe a class of simulation languages in which the inter event distribution may be generally distributed, but the next state is only dependent on the current state. Taking this as the definition, our approach is within the GSMP class, and can express GSMP models. However, ....

....execution policy does not preclude efficient solution. We propose such a generalized execution policy. A consequence of this is the relaxation of what we call the constant work assumption. For example, consider the formulation of the generalized semi Markov process, or GSMP, given in [5, 10]. Once an event is enabled, the event rate can change only by a constant as a function of model state; that is, the delay distribution can change only by a linear scaling. What is specifically absent is the ability for the delay to change in an arbitrary way while the event is still enabled. ....

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C.G.Cassandras. Discrete Event Systems: Modeling and Performance Analysis. Aksen Associates Incorporated Publishers, Homewood, IL, USA, 1993.

....model, the output (i.e. the plant profit) depends on the functioning state of all the working machines less the cost incurred for machines being repaired. Therefore, we need to define two functions that will be a part of the output function. The first is an efficiency function, h : 1, 2, N [0, 1]; where h(i) is the efficiency of a machine in state i. The second function is a cost to repair function, c : 2, F R ; where c(i) is the cost (per time unit) to repair a machine that is in state i (c is obviously defined for i 1 since there is no sense in repairing a correctly ....

....of the system and determines whether to send R machines to repair, or not, the control action) applying the following control rule: If input is less then wPN, then send R machines to repair. This controller is defined by the two parameters w and R. The w parameter can be any real number in [0,1] and the R parameter can be any non negative real number. If w = 0 this means that the threshold for the input is zero, i.e. the controller will never send a machine to repair. The opposite extreme corresponds to a value of w = 1 which means that the threshold is PN, i.e. the maximum profit ....

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C.G. Cassandras, Discrete Event Systems: Modeling and Performance Analysis, Richard D. Irwin and Aksen Associates Inc. Publishers, Boston, MA, 1993.

....exist. In fact the PSA for DEDS rely heavily on the smoothing properties of the above mentioned average or the mean of the variables. Perturbation analysis of DEDS is the hot topic of research in recent times [Ho91] Gla94] Information and references on discrete event systems can be found in [Cas93], Ste94] Lin98] After getting the sensitivities of the system parameters the designer can fix the most sensitive parameters. This sensitivities can be used to develop an optimal supervisory control of DEDS. To analyze, control, and improve the performance of the system dynamically PSA, ....

Christos G. Cassandras. Discrete Event Systems: Modeling and Performance Analysis. Richard D. Irwin, Inc., and Aksen Associates, Inc., 1993.

....L(#) is called the sample performance function. Its expected value as function, #(#) E[L(#) is assumed to be well defined and finite valued. In the context of DEDS, for a given fixed # # #, IPA computes the sample derivative L # (#) which is said to be unbiased if E[L # (#) # # (#) [3, 4] 3 . Various su#cient conditions for the unbiasedness of IPA have been derived. Here, we use the formulation set forth in [13] Lemma A2, p.70, reproduced below. Condition 4.1 (Su#cient unbiasedness conditions) Suppose the following two conditions hold: i) For every # # #, the sample ....

C.G. Cassandras, Discrete Event Systems: Modeling and Performance Analysis, Aksen Associates Publishers, Irwin, Boston, MA, 1993.

....amount than the bw # ratio term. These results also show that for different scenarios, different value functions might perform better. The averages for all three loading levels and both values of can be found in [18] Using the data collected from the simulations, the 950 confidence interval [3, 9] (min, max) for the average normalized sum of the weighted priorities was calculated for each of the loading levels and each of the value functions evaluated. Each of the data points is for a W W #W U ratio of 1 (log 10 (W W #W U ) 0) This was done for the normalized values. As a representative ....

C. Cassandras, Discrete Event Systems: Modeling and Performance Analysis," Irwin, Homewood, IL, 1993.

....posed modeling language in section 2 we will return to this point. As mentioned above various notations have been developed for modeling and (performance) analysis, see for example [9] for a recent overview. Most of them belong to the class of formalism for discrete event dynamic systems (DEDS) [6]. DEDS consider systems which are characterized by discrete states and atomic state changes among states. This is quite natural in a business context, e.g. in bookkeeping where quantities are discrete (money, stock of materials) and entries are made completely or not. We take this point of view as ....

C. Cassandras. Discrete event systems: modeling and performance analysis. Irwin, Aksen, 1993.

....systems (DEDS) models. DEDS can be used to model the event driven systems common to man made systems, as well as to supervise subsystems of time driven natural processes, such as temperature decay and the dynamics and kinematics of mass motion, making DEDS a powerful modeling paradigm [1]. In response to industrial demand for DEDS models, several simulation packages have been developed, and their use has greatly helped the understanding of these systems and the development of control policies for them. However, suitable control policies for DEDS are generally difficult to derive ....

.... q q q=0 Figure 3 Inventory system event graph European Symposium on Intelligent Techniques, March 20 21, 1997, Bari, Italy ERUDIT Service Center, c o ELITE Foundation, Promenade 9, 52076 Aachen, Germany 129 The mean inter demand time ( E ) is considered to be the only CPE and the interval [1,5] is considered to be the CPE subspace. Therefore, an exploration of this subspace is done by running the GA for E =1,2,3,4,5. The optimal s and a, along with the cost (per part) for these five cases are given in Table 1. From these results we can see that there is a general trend, and that they ....

Cassandras, C.G., Discrete Event Systems: Modeling and Performance Analysis, Richard D. Irwin and Aksen Associates Inc. Publishers, Boston, MA, 1993.

....with respect to #. Accordingly, we will be concerned with estimating the derivatives # # V (#) and # # W (#) via their IPA estimators the sample derivatives L # V (#) and L # W (#) respectively. Comprehensive discussions of IPA derivatives and their applications can be found in [5, 2]. Here, we merely outline the main results germane to the present discussion. Let L(#) be a sample performance function of #, and let #(#) E[L(#) denote its expectation. The IPA estimator of # # (#) also called the IPA derivative) is defined as the sample derivative, L # (#) An IPA ....

C.G. Cassandras, Discrete Event Systems: Modeling and Performance Analysis, Aksen Associates, Irwin, Boston, MA, 1993.

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Christos Cassandras. Discrete Event Systems: Modeling and Performance Analysis.Irwinand Aksen, Boston, Massachusetts, 1993.

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Cassandras, C.G. (1993). Discrete Event Systems: Modeling and Performance Analysis, Irwin, Boston.

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C. Cassandras, Discrete-Event Systems: Modeling and Performance Analysis. Homewood, IL: Irwin, 1993.

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C. G. Cassandras. Discrete Event Systems: Modeling and Performance Analysis. Asken Associates, 1993.

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C. G. Cassandras. Discrete Event Systems: Modeling and Performance Analysis. Asken Associates, 1993. 25

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C. Cassandras, "Discrete event systems, modeling and performance analysis", Irwin, Homewood IL, 1993.

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Cassandras, C., "Discrete event systems, modeling and performance analysis", Irwin, Homewood IL, 1993.

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C. G. Cassandras. Discrete Event Systems: Modeling and Performance Analysis. Aksen Associates, Boston, MA, 1993.

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C.G. Cassandras, Discrete Event Systems -- Modeling and Performance Analysis (Richard D. Irwin and Aksen Associates, 1993).

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C.G. Cassandras, Discrete Event Systems: Modeling and Performance Analysis, Aksen Associates Publishers, Irwin, Boston, MA, 1993.

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