N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice Hall, New Jersey, 1992.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... sample of such work are the following papers: HJ90, JY95, LS91, HS86, BBS95, vGSS95, CSZ92] These papers study concepts like probabilistic bisimulation [LS91]probabilistic testing [JY95] and the relationship with (probabilistic) modal logics [HS86] Probabilistic Petri nets [Mar89, VN92] add Markov chains to the underlying Petri net model. This area has a well developed suite of algorithms for performance evaluation. Investigations into the behaviour of probabilistic systems have also been carried out in the context of IO Automata [Seg95, WSS97] In contrast to the above body ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice-Hall Inc, 1992.

....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 ....

N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems, Prentice-Hall, Englewood Cliffs, NJ, 1992. 19

....of probabilistic processes, has been explored extensively using di#erent models of concurrency. Probabilistic process algebras add randomness to the process algebra models see for example [Han94, HJ94, JL91, JY95, LS91, HS86, BBS95, vGSS95, CSZ92, Cle94] Probabilistic Petri nets [Mar89, VN92] add Markov chains to the underlying Petri net model. Probabilistic extensions of IO Automata [Seg95, WSS97] have also been developed. The verification community has been active in developing model checking tools for probabilistic systems, for example [BLL 96, BdA95, BCHG 97, CY95, HK97] ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice-Hall Inc, 1992.

....to bypass the GUI. In this case, MIDAS connects the user to the Illustra Query Interface. 1.2. 3 Decision Support One of the steps in planning the operation of production is production planning which determines the quantity of products to be manufactured during a particular period of time [VN92]. The decision is based on the available resources such as machine capacities, raw material and tools that are available or can be acquired. Next the scheduler tries to prepare a static schedule that has good on time performance and which reduces the work inprocess (WIP) inventory. A heuristic ....

....generation programs namely Plant View , Process View and Lot View that give orthogonal views of the plant operation are included. The view generation programs are generic and not specific to a plant and they interact only with the MIDAS database to retrieve information. 1. 3 Related Work The book [VN92] gives a comprehensive overview of automated manufacturing systems and a detailed description of flexible manufacturing systems. An introduction to computer automated manufacturing is given in [Powe87] The book [Lugg91] gives a general description of flexible manufacturing cells and systems, and ....

[Article contains additional citation context not shown here]

N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems , Prentice-Hall of India, 1994.

.... properties pertaining to the static aspects of the process s definition, and (ii) behavioral properties pertaining to the dynamic aspects of the process observed during its execution[7, 17] the latter being useful in deriving summary measures (e.g. cycle time) for performance evaluation studies[25]. 3.1 Current Research Approaches for Establishing Control Flow Correctness There are two approaches to ensuring correct models (i) build it correctly, or (ii) check it completely. The former relies on strict grammatical rules that govern the composition of the various elements in the model, ....

Viswanadham, N. and Narahari, Y. Performance Modeling of Automated Manufacturing Systems. Prentice-Hall, Inc., 1992.

....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 ....

N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems, Prentice-Hall, Englewood Clis, NJ, 1992.

....e.g. tools, control systems, and their main focus is on the steady state analysis of discrete manufacturing systems. Moreover, the model proposed by Albino et al. 5] did not include material handling systems. For more complete description of performance models of manufacturing systems, see [6] [8]. The popularity of steady state analysis of manufacturing systems stems from its computational simplicity. Although the steady state performance analysis is important, the advantages of transient analysis of the system cannot be overlooked. Narahari and Viswanadham [9] discussed several ....

....if the performance of these components highly affects the system performance. It was assumed that the failure and repair times of the machines and MHS were exponential random variables. However, in industrial systems, the time distributions are arbitrary which can be handled semi Markov processes [8]. A semiMarkov process is an extension of the Markov process where the Assumptions 2 and 3 are relaxed. As a result, failure and repair times are no longer constrained to be exponentially distributed. A state transition may now occur at any time, and the failure repair time can follow an arbitrary ....

N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems. Englewood Cliffs, NJ: Prentice Hall, 1992.

.... sample of such work is represented by the following papers: HJ90, JY95, LS91, HS86, BBS95, vGSS95, CSZ92] These papers study concepts like probabilistic bisimulation [LS91]probabilistic testing [JY95] and the relationship with (probabilistic) modal logics [HS86] Probabilistic Petri nets [Mar89, VN92] add Markov chains to the underlying Petri net model. This area has a well developed suite of algorithms for performance evaluation. Investigations into the behaviour of probabilistic systems have also been carried out in the context of IO Automata [Seg95, WSS97] In contrast to the above body ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice-Hall Inc, 1992.

....of probabilistic processes, has been explored extensively using di erent models of concurrency. Probabilistic process algebras add randomness to the process algebra models see for example [Han94, HJ94, JL91, JY95, LS91, HS86, BBS95, vGSS95, CSZ92, Cle94] Probabilistic Petri nets [Mar89, VN92] add Markov chains to the underlying Petri net model. Probabilistic extensions of IO Automata [Seg95, WSS97] have also been developed. The veri cation community has been active in developing model checking tools for probabilistic systems, for example [BLL 96, BdA95, BK97, CY95, HK97] By and ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice-Hall Inc, 1992.

....networks, performance evaluation, and risk analysis typically require a number of tools for the specification and simulation of systems, and to compute statistics of interest. The modelling and simulation tasks usually require modular models, which are often variations of stochastic Petri nets [36]. The computation of statistics relies on the underlying Markov chain associated to the Petri net specification. ffl Pattern recognition applications, depending on whether they focus on one dimensional signals, such as for speech recognition, or multidimensional ones, as in image analysis and ....

.... issues are almost never addressed by either statisticians or control engineers, and as a consequence, probabilistic and statistical techniques are used only rarely in the analysis of large scale systems (except in the area of performance evaluation, see below) Stochastic Petri net models [36, 24] are often used to specify stochastic systems, in applications such as queing networks with synchronization, or fault tolerance studies. They are commonly employed to evaluate statistics of interest in performance evaluation. However, such computations rely on the underlying Markov chain of the ....

N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems, Prentice Hall, Englewood Cliffs, NJ, 1992.

....measures of production systems, such as flow time, work in process inventory, and production rates, has received little rigorous treatment in the literature. Although a number of analytical models have been proposed for the general performance evaluation of manufacturing systems [2] 3] [10], none of these models deals explicitly with the relationships between batch sizes and performance. In the queueing literature, a significant body of work exists on queues with bulk arrivals and bulk service times [8] However, exact results exist only for simple models. None of these models ....

Viswanadham, N. and Y. Narahari, Performance Modeling of Automated Manufacturing Systems, Prentice Hall, Englewood Cliffs, New Jersey, 1992.

....largely in the context of exact equivalence of probabilistic processes, has been explored extensively using different models of concurrency. Probabilistic process algebras add randomness to the process algebra models see for example [25, 24, 30, 31, 36, 26, 4, 47, 9, 8] Probabilistic Petri nets [39, 48] add Markov chains to the underlying Petri net model. Probabilistic extensions of IO Automata [45, 49] have also been developed. By and large, the above work focuses on discrete state systems. An early investigation into continuous state spaces was by deVink and Rutten [10] While their work ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. PrenticeHall Inc, 1992.

....under grants CCR 9201195 and NCR 9527163. z The work of the third author was supported in part by the National Science Foundation under grant NSF EEC 94 18765. i 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 difficulties encountered while using stochastic Petri nets is that the underlying reachability graph tends to be very large in practical problems. Drawing a parallel with fluid flow approximations in performance analysis of queueing systems [3, 7, 11] SPNs have been extended to ....

N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems, Prentice-Hall, Englewood Cliffs, NJ, 1992. 15

....deadlockfree scheduling policies. The work of Viswanadham et al. [18] of Banaszak and Krogh [1] Wysk et al. 16] and, more recently, of Hsieh and Chang [8] and Ezpeleta et al. 3] are remarkable contributions in this direction. In particular, using Petri net (PN) model Viswanadham et al. [17], 18] propose some techniques for deadlock prevention and avoidance in FMS s. The authors define a deadlock prevention policy using an exhaustive path analysis of the reachability graph of the PN. However, this approach is feasible for reasonably small systems only. The authors also introduce a ....

N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems. Englewood Cliffs, NJ: Prentice Hall, 1992.

.... (FMS) and flexible assembly systems (FAS) the interested reader is referred to a review paper by Papadopoulos and Heavey [13] and some recently published books, such as Askin and Standridge [2] Buzacott and Shanthikumar [3] Gershwin [7] Papadopoulos et al. 14] Viswanadham and Narahari [16], and Altiok [1] The difficulties of the problem have led us into the deployment of an arsenal of different methods for determining the optimal design of the production line. These methods involve both the estimation of line throughput and the calculation of the optimal line design variables. In ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice Hall, New Jersey, 1992. 5

....and Viterbi algorithm. 3.1 Fault information contained in fault graphs We assume the following is available from reliability analyses (fmea and the like) ffl The graph of possible individual fault propagations. As mentioned in the introduction, this graph is typically a component graph [19], which provides us with information on how faults on the different components can occur and are linked together. We assume that, from this graph, a Markov chain can be explicitly found, which defines fault states and transition probabilities. In the particular case where the component graph is an ....

....components can occur and are linked together. We assume that, from this graph, a Markov chain can be explicitly found, which defines fault states and transition probabilities. In the particular case where the component graph is an exponential timed stochastic Petri net (etpn ) it is possible [19] to derive a graph, called the marking graph, which vertices are the states of a Markov chain which represents the faulty states of the system, as depicted in figure 1. As repair or self repair can also be considered, this directed graph can contain circuits. In any case, we assume an initial ....

[Article contains additional citation context not shown here]

N. Viswanadham and Y. Narahari (1992). Performance Modeling of Automated Manufacturing Systems. Prentice Hall Information and System Sciences Series.

....networks, performance evaluation, and risk analysis typically require a number of tools for the specification and simulation of systems, and to compute statistics of interest. The modelling and simulation tasks usually require modular models, which are often variations of stochastic Petri nets [1]. The computation of statistics relies on the underlying Markov chain associated to the Petri net specification. ffl Pattern recognition applications, depending on whether they focus on one dimensional signals, such as for speech recognition, or multidimensional ones, as in image analysis and ....

.... issues are almost never addressed by either statisticians or control engineers, and as a consequence, probabilistic and statistical techniques are used only rarely in the analysis of large scale systems (except in the area of performance evaluation, see below) Stochastic Petri net models [1, 8] are often used to specify stochastic systems, in applications such as queing networks with synchronization, or fault tolerance studies. They are commonly employed to evaluate statistics of interest in performance evaluation. However, such computations rely on the underlying Markov chain of the ....

N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems. Englewood Cliffs, NJ: Prentice Hall, 1992.

....model. 3) The verification community has been very active and there has been significant activity in developing model checking tools for probabilistic systems, for example [3, 13, 6, 19, 35] Our work is not directedly related but should be seen as a complementary tool. 4) Stochastic petri nets [43, 56] add Markov chains to the underlying Petri net model. This area has a well developed suite of algorithms for performance evaluation. In the full version of this paper, we show how to encode a variant of stochastic Petri nets into Probabilistic cc. 5) Probabilistic studies have also been carried ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice-Hall Inc., 1992.

....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 difficulties encountered while using stochastic Petri nets is that the underlying reachability graph tends to be very large in practical problems. Drawing a parallel with fluid flow approximations in performance analysis of queueing systems [3, 7, 11] SPNs have been extended to ....

N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems, Prentice-Hall, Englewood Cliffs, NJ, 1992.

....framework of (different) semantic theories of (different) process algebras (to name but a few, see [HJ90, JY95, LS91, HS86, BBS95, vGSS95, CSZ92] e.g. bisimulation, theories of (probabilistic) testing, relationship with (probabilistic) modal logics etc. Probabilistic Petri nets [Mar89, VN92] add Markov chains to the underlying Petri net model. This area has a well developed suite of algorithms for performance evaluation. Probabilistic studies have also been carried out in the context of IO Automata [Seg95, WSS97] In contrast to the above body of research the primary theme of this ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. PrenticeHall Inc., 1992.

....networks, performance evaluation, and risk analysis typically require a number of tools for the specification and simulation of systems, and to compute statistics of interest. The modelling and simulation tasks usually require modular models, which are often variations of stochastic Petri nets [36]. The computation of statistics relies on the underlying Markov chain associated to the Petri net specification. ffl Pattern recognition applications, depending on whether they focus on one dimensional signals, such as for speech recognition, or multidimensional ones, as in image analysis and ....

.... issues are almost never addressed by either statisticians or control engineers, and as a consequence, probabilistic and statistical techniques are used only rarely in the analysis of large scale systems (except in the area of performance evaluation, see below) Stochastic Petri net models [36, 24] are often used to specify stochastic systems, in applications such as queing networks with synchronization, or fault tolerance studies. They are commonly employed to evaluate statistics of interest in performance evaluation. However, such computations rely on the underlying Markov chain of the ....

N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems, Prentice Hall, Englewood Cliffs, NJ, 1992.

....1 Gamma r. X is not free in P; Q. Since the random variables are hidden, we get the expected laws: P r P = P (absorption) P r Q = Q (1 Gammar) P (commutativity) P r Q) sR = P rs (Q s(1 Gammar) 1 Gammars R) associativity) Example 2. 8 We model the Probabilistic Petri nets described in [45, 62]. In these nets, places are responsible for the probabilistic behavior, while transitions impose constraints to ensure correct behavior. Nets are 1 safe, so a place may contain at most one token. Temporal evolution is discrete (modeled here by a recursive call) At each time tick, a place with a ....

....model. 3) The verification community has been very active and there has been significant activity in developing model checking tools for probabilistic systems, for example [13, 6, 20, 38] Our work is not directedly related but should be seen as a complementary tool. 4) Probabilistic Petri nets [45, 62] add Markov chains to the underlying Petri net model. This area has a well developed suite of algorithms for performance evaluation. Example 2.8 shows how to represent such nets in Probabilistic cc. 5) Probabilistic studies have also been carried out in the context of IO Automata [58, 63] The ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice-Hall Inc., 1992.

....model is fully abstract. Related work The role of probability has been extensively studied in the context of several models of concurrency. Typically, these studies have involved a marriage of a concurrent computation model with a model of probability. For example, stochastic Petri nets [Mar89, VN92] add Markov chains to the underlying Petri net model. Similarly probabilistic process algebras add a notion of randomness to the underlying process algebra model. This theory is well developed and is primarily about the interaction between probabilityand non determinism, see for example [HJ90, ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice-Hall Inc., 1992. This article was processed using the L A T E X macro package with LLNCS style

....in the western way of perceiving the world since the work of ancient Greek philosophers Heraclitus and Parmenides. These two kinds of thinking and representing the way change is modeled can be found, for instance, in the research of Gershwin [17] signal or stream oriented) and in Petri nets [25][13] and most approaches to simulation (object oriented) The former has been favored by those who try to apply Control Theory concepts to MS problems, and the later is preferred by those who attempt to directly mimic the workings of physical systems. On the other hand, when considering the ....

N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems, Prentice Hall, 1992.

....a marriage of a concurrrent computation model with a model of probability. For example, stochastic Petri nets [Mar89] add Markov chains to the underlying Petri net model the success of this approach is vouched for by the existence of several software tools to compute performance statistics [VN92] Similarly probabilistic process algebras add a notion of randomness to the underlying process algebra model. This theory is well developed and is primarily about the interaction between probability and non determinism, see for example [HJ90, vGSST90, JY95, LS91, HS86, CSZ92] These studies have ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice-Hall Inc., 1992.

....system simulation, stochastic Petri nets. 1 Introduction Discrete event dynamic systems are commonplace, and discrete state models are normally used to study their behavior. Ordinary and stochastic Petri nets, for example, provide a convenient and concise method of describing these systems [4, 8, 11, 22, 25]. However, the underlying state space of these models tends to be extremely large in practical modeling applications, often forcing us to seek approximate solution methods. An example is the fluid flow approximations in performance analysis of queueing systems [5, 15, 21] where a large number of ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice-Hall, Englewood Cliffs, NJ, 1992.

....models are increasingly used in the performance analysis of communications [3, 11, 14] 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, 18, 20]. It is natural to extend the stochastic Petri net (SPN) 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 [19] by allowing the level ....

N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems, Prentice-Hall, Englewood Cliffs, NJ, 1992.

.... with the help of SPNs showing that a formalism and a technique originally developed for the analysis of parallel and distributed computing systems can be conveniently employed also in application fields that are apparently quite different, but that present instead quite a lot of commonality [23, 38, 154, 187, 164]. Finally SPNs have been found useful in the analysis of systems affected by failures as it is shown in [4, 26] ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice Hall, 1992.

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N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice Hall, Englewood Cli#s, NJ, 1992.

....at the Digital Equipment Corporation. 6 2.3.2 Analytical Performance Models Models of supply chains in a dynamic and stochastic environment consider the network as a discrete event dynamic system. Such systems can be studied as Markov chains, stochastic Petri nets and queueing network models [42, 33]. Malone and Smith [27] in their study, have looked at organizational and coordination structures, which constitute a key element of any business process. Raghavan and Viswanadham [32] discuss performance modeling and dynamic scheduling of make to order supply chains using fork join queueing ....

N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice Hall, Englewood Cliffs, NJ, 1992.

....of PCB fabrication process considered. These software packages have been developed at the department of Computer Science and Automation, Indian Institute of Science, Bangalore. 1.3. 1 Open Queueing Network Analyzer This tool analyses open Jackson queueing networks with multiserver stations [12]. The inputs to the package are [5] ffl Number of stations in the network ffl Number of servers at each station 3 ffl External arrival rate of jobs at each station ffl Service rate of each station ffl Routing probabilities ffl Squared Coefficient Variation (SCV) of arrival rate ffl SCV ....

Viswanadham N. and Narahari Y. (1992). Performance Modeling of Automated Manufacturing Systems. Prentice Hall, Englewood Cliffs. 19

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N. Viswanadham and Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice Hall, New Jersey, 1992.

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N. Viswanadham and Y. Narahari, Performance Modeling of Automated Manufacturing Systems, Prentice Hall, Englewood Cli#s, NJ, 1992.

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Viswanadham, N. and Y. Narahari. 1992. Performance Modeling of Automated Manufacturing Systems, Pretice-Hall, New Jersey.

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