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Готові списки джерел за темами / Discrete-Event models

Добірка наукової літератури з теми "Discrete-Event models"

Автор: Grafiati

Опубліковано: 1 лютого 2023

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Статті в журналах з теми "Discrete-Event models":

1

Inan, K. M., and P. P. Varaiya. "Algebras of discrete event models." Proceedings of the IEEE 77, no. 1 (1989): 24–38. http://dx.doi.org/10.1109/5.21068.

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2

Savenkov, K. O., and R. L. Smeliansky. "Scaling down discrete-event simulation models." Programming and Computer Software 32, no. 6 (December 2006): 308–16. http://dx.doi.org/10.1134/s036176880606003x.

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3

Mareels, I. M. Y. "Dynamic models and discrete event simulation." Automatica 27, no. 3 (May 1991): 589–90. http://dx.doi.org/10.1016/0005-1098(91)90125-l.

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4

Sampath, M., R. Sengupta, S. Lafortune, K. Sinnamohideen, and D. C. Teneketzis. "Failure diagnosis using discrete-event models." IEEE Transactions on Control Systems Technology 4, no. 2 (March 1996): 105–24. http://dx.doi.org/10.1109/87.486338.

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5

Cao, X. R., and Y. C. Ho. "Models of discrete event dynamic systems." IEEE Control Systems Magazine 10, no. 4 (June 1990): 69–76. http://dx.doi.org/10.1109/37.56280.

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6

Kettenis, Dirk L. "Discrete event systems: Models and applications." European Journal of Operational Research 37, no. 3 (December 1988): 417–18. http://dx.doi.org/10.1016/0377-2217(88)90212-3.

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7

Potekhin, A. I., S. A. Branishtov, and S. K. Kuznetsov. "Discrete-event models of a railway network." Automation and Remote Control 77, no. 2 (February 2016): 344–55. http://dx.doi.org/10.1134/s0005117916020107.

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8

Rangel, Cíntia De Lima, João José De Assis Rangel, Eduardo Shimoda, and Janaína Ribeiro Do Nascimento. "DISCRETE-EVENT SIMULATION MODELS FOR DIDACTIC SUPPORT." Brazilian Journal of Operations & Production Management 13, no. 3 (September 29, 2016): 300. http://dx.doi.org/10.14488/bjopm.2016.v13.n3.a7.

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This paper presents the evaluation of a discrete event simulation model designed to be used as a didactic aid instrument in classes of a technical course on high school. The simulation model was developed using the free version of the Arena software. Among the results obtained, it was found that applying this model as a didactic resource in classes has enabled an increase of quality in the students’ learning. This result was even more significant concerning students with average grades below 6. In these cases, with the help of the simulator, students of worse educational achievement obtained performance close to the ones that had average grades equal to or higher than 8.

9

Bagrodia, R. L. "Parallel languages for discrete-event simulation models." IEEE Computational Science and Engineering 5, no. 2 (1998): 27–38. http://dx.doi.org/10.1109/99.683737.

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10

Chan, Wai Kin (Victor), and Lee Schruben. "Optimization Models of Discrete-Event System Dynamics." Operations Research 56, no. 5 (October 2008): 1218–37. http://dx.doi.org/10.1287/opre.1080.0559.

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Дисертації з теми "Discrete-Event models":

1

Kuljis, Jasminka. "User interfaces and discrete event simulation models." Thesis, London School of Economics and Political Science (University of London), 1995. http://etheses.lse.ac.uk/1380/.

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A user interface is critical to the success of any computer-based system. Numerous studies have shown that interface design has a significant influence on factors such as learning time, performance speed, error rates, and user satisfaction. Computer-based simulation modelling is one of the domains that is particularly demanding in terms of user interfaces. It is also an area that often pioneers new technologies that are not necessarily previously researched in terms of human-computer interaction. The dissertation describes research into user interfaces for discrete event simulation. Issues that influence the 'usability' of such systems are examined. Several representative systems were investigated in order to generate some general assumptions with respect to those characteristics of user interfaces employed in simulation systems. A case study was carried out to gain practical experience and to identify possible problems that can be encountered in user interface development. There is a need for simulation systems that can support the developments of simulation models in many domains, which are not supported by contemporary simulation software. Many user interface deficiencies are discovered and reported. On the basis of findings in this research, proposals are made on how user interfaces for simulation systems can be enhanced to match better the needs specific to the domain of simulation modelling, and on how better to support users in simulation model developments. Such improvements in user interfaces that better support users in simulation model developments could achieve a reduction in the amount of time needed to learn simulation systems, support retention of learned concepts over time, reduce the number of errors during interaction, reduce the amount of time and effort needed for model development, and provide greater user satisfaction.

2

Domingo, Lizza Tandoc. "Formal methods in specifying discrete event simulation models." Thesis, London School of Economics and Political Science (University of London), 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263339.

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3

Derrick, Emory Joseph. "Conceptual frameworks for discrete event simulation modeling." Thesis, Virginia Tech, 1988. http://hdl.handle.net/10919/43840.

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4

Elgmati, Entisar. "Additive intensity models for discrete time recurrent event data." Thesis, University of Newcastle Upon Tyne, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556142.

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The thesis considers the Aalen additive regression model for recurrent event data. The model itself, estimation of the cumulative regression functions, testing procedures, checking goodness of fit and inclusion of dynamic covariates in the model are reviewed. A disadvantage of this model is that estimates of the conditional probabilities are not constrained to lie between zero and one, therefore a model with logistic intensity is considered. Results under the logistic model are shown to be qualitatively similar to those under the additive model. The additive model is extended to incorporate the possibility of spatial or spatio-temporal clustering, possibly caused by unobserved environmental factors or infectivity. Various tests for the presence of clustering are described and implemented. The issue of frailty modelling and its connection to dynamic modelling is presented and examined. We show that frailty and dynamic models are almost indistinguishable in terms of residual summary plots. A graphical procedure based on the property that the covariance between martingale residuals at time to and t > to is independent of t is proposed and supplemented by a formal test statistic to investigate the adequacy of the fitted models. The results can be used to compare models and to check the validity of the model being tested. Also we investigate properties under various types of model misspecification. All our works are illustrated using two sets of data measuring daily prevalence and incidence of infant diarrhoea in Salvador, Brazil. Significant clustering is identified in the data. We investigate risk factors for diarrhoea and there is strong evidence of dynamic effects being important, implying heterogeneity between individuals not explained by measured socio- economic and environmental factors.

5

Soueidan, Hayssam. "Discrete event modeling and analysis for systems biology models." Thesis, Bordeaux 1, 2009. http://www.theses.fr/2009BOR13916/document.

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Les travaux effectués durant cette thèse portent sur la spécification, l'analyse et l'application de systèmes a événements discrets pour la modélisation de processus biologiques stochastiques en biologie des systèmes. Le point de départ de cette thèse est le langage de modélisation AltaRica, que nous étendons afin de permettre de décrire des événements temporisés selon des distributions de probabilités quelconques (dégénérées, discrètes et continues). Nous définissons ensuite la sémantique de ce langage en terme d'automates de mode stochastiques et présentons trois opérations de compositions permettant de modéliser des systèmes hiérarchiques avec événements synchronisés et partage de valeurs via un mécanisme de connexion. Nous donnons ensuite au automates de mode stochastiques une sémantique en termes de systèmes de transitions dont les transitions sont étiquetées par des distributions de probabilités et des probabilités de transitions instantanées. Nous caractérisons ensuite 6 sous classes de ces systèmes de transitions et donnons pour chacune de ces classes un algorithme de simulation ainsi qu'une mesure de probabilité sur les chemins finis. Nous montrons que pour certaines de ces classes, notre sémantique est conforme avec les mesures de probabilité de chemin usuellement associées aux chaînes de Markov a temps discret, a temps continu et aux processus semi-Markoviens généralisés. Nous abordons ensuite le problème de la réutilisation de modèles continus existant dans un système discret. Nous donnons une méthode d'abstraction permettant de représenter un ensemble de trajectoires bornées ou non d'un modèle continu sous forme d'un système de transition stochastique fini. A travers des exemples tirés de la littérature, nous montrons que notre abstraction préserve les propriétés "qualitatives" (par exemple oscillations, hystérie) des modèles continus et qu'une comparaison entre trajectoires basée sur leurs représentations en termes de systèmes de transitions permet de regrouper les trajectoires en fonction de comportements qualitatifs plus fins que ceux permis par la théorie des bifurcations. Finalement, nous étudions a l'aide de ces modèles des processus liés a la division cellulaire chez les levures. En particulier, nous définissons un modèle pour le vieillissement cellulaire dans une population de levure où le comportement individuel d'une cellule est régi par une équation différentielle ordinaire et où le processus de division est régi par un système de transition. Nous montrons a l'aide de ce modèle que la survie d'une population de levure de type Schizosaccharomyces Pombe, qui se divisent par une fission médiane, n'est possible que grâce a un mécanisme de distribution non symétrique des dégâts oxydatifs entre la progéniture et la cellule souche. Cette hypothèse fut validée expérimentalement lors d'une collaboration avec le laboratoire de micro-biologie de Göteborg
A general goal of systems biology is to acquire a detailed understanding of the dynamics of living systems by relating functional properties of whole systems with the interactions of their constituents. Often this goal is tackled through computer simulation. A number of different formalisms are currently used to construct numerical representations of biological systems, and a certain wealth of models is proposed using ad hoc methods. There arises an interesting question of to what extent these models can be reused and composed, together or in a larger framework. In this thesis, we propose BioRica as a means to circumvent the difficulty of incorporating disparate approaches in the same modeling study. BioRica is an extension of the AltaRica specification language to describe hierarchical non-deterministic General Semi-Markov processes. We first extend the syntax and automata semantics of AltaRica in order to account for stochastic labeling. We then provide a semantics to BioRica programs in terms of stochastic transition systems, that are transition systems with stochastic labeling. We then develop numerical methods to symbolically compute the probability of a given finite path in a stochastic transition systems. We then define algorithms and rules to compile a BioRica system into a stand alone C++ simulator that simulates the underlying stochastic process. We also present language extensions that enables the modeler to include into a BioRica hierarchical systems nodes that use numerical libraries (e.g. Mathematica, Matlab, GSL). Such nodes can be used to perform numerical integration or flux balance analysis during discrete event simulation. We then consider the problem of using models with uncertain parameter values. Quantitative models in Systems Biology depend on a large number of free parameters, whose values completely determine behavior of models. Some range of parameter values produce similar system dynamics, making it possible to define general trends for trajectories of the system (e.g. oscillating behavior) for some parameter values. In this work, we defined an automata-based formalism to describe the qualitative behavior of systems’ dynamics. Qualitative behaviors are represented by finite transition systems whose states contain predicate valuation and whose transitions are labeled by probabilistic delays. We provide algorithms to automatically build such automata representation by using random sampling over the parameter space and algorithms to compare and cluster the resulting qualitative transition system. Finally, we validate our approach by studying a rejuvenation effect in yeasts cells population by using a hierarchical population model defined in BioRica. Models of ageing for yeast cells aim to provide insight into the general biological processes of ageing. For this study, we used the BioRica framework to generate a hierarchical simulation tool that allows dynamic creation of entities during simulation. The predictions of our hierarchical mathematical model has been validated experimentally by the micro-biology laboratory of Gothenburg

6

Foong, Yew Chong. "Airlift operations modeling using Discrete Event Simulation (DES)." Thesis, Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Dec/09Dec%5FFoong.pdf.

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Thesis (M.S. in Modeling, Virtual Environments, and Simulation (MOVES))--Naval Postgraduate School, December 2009.
Thesis Advisor(s): Buss, Arnold. Second Reader: Meyer, David. "December 2009." Description based on title screen as viewed on January 28, 2010. Author(s) subject terms: Discrete Event Simulation, airlift operation. Includes bibliographical references (p. 145-147). Also available in print.

7

Goodman, Daniel. "Enhancing discrete event modelling by interfacing expert systems and simulation models." Thesis, London School of Economics and Political Science (University of London), 1993. http://etheses.lse.ac.uk/3050/.

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This thesis investigates the representation of operational decision makers within simulation modelling. Artificial Intelligence concepts, such as expert systems focus on the problem of representing, in high-level code, complex real-world decision making problems. The author therefore proposes that the use of expert system technology may provide an improved means of representing operational decision tasks and that as a consequence, apriori possibilities may exist in the context of model experimentation based on alternative operational policies. The thesis further investigates the nature of operational decision making and the potential need to represent within a model, inter-dependencies between decision makers. A prototype system called ESSIM is developed which comprises of two interlinked components, a discrete event simulation module and expert system module. The benefits of the proposed approach are then assessed by comparing the functionally of ESSIM with conventional modelling techniques. The comparison is carried out by developing three alternative models of an automated container port, one of these using ESSIM. Experiments were then devised and executed which seek to draw conclusions on the thesis proposal.

8

Alt, Aaron J. "Profile Driven Partitioning Of Parallel Simulation Models." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1407406955.

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9

Razavi, Behnam. "Decision analysis models for aircraft engine maintenance planning using discrete event simulation." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/52466.

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With stringent standards for materials, manufacturing, operation, and quality control, jet engines in use on commercial aircraft are very reliable. It is not uncommon for engines to operate for thousands of hours before being scheduled for inspection, service or repair. However, due to required maintenance and unexpected failures aircraft must be periodically grounded and their engines attended to. The tasks of maintenance and repair without optimal planning can be costly and result in prolonged maintenance times, reduced availability and possible flight delays. These factors have a negative impact on both the airline operators and the passengers alike. Aircraft manufacturers and maintainers, who provide after sale services, see significant benefits in constantly improving health management and maintenance practices by deploying the most effective maintenance strategies. Maintenance is seen as an imposed cost that ought to be minimized. Airlines must evaluate new technologies and their possible role in reducing the long term expenditure for operating a fleet of aircraft throughout its life cycle. A significant share of these expenses goes towards maintenance of these aircraft, especially their engines. This study presents a model-based integrated decision making system for aircraft engine maintenance planning. The goal is to determine the optimum number of engines on an aircraft for maintenance based on logged engine operation data in order to maximize the use of estimated remaining time to the next service as well as to minimize the duration of downtime. To achieve this, engine condition is used in a set of preliminary Discrete Event Simulation (DES) models to evaluate and provide the most effective maintenance policies for the aircraft engines. To assess options for making decisions, a comprehensive model is developed based on the integration of the smaller preliminary maintenance models for one, two, three and four engine maintenance cases. Results from these analyses determine the optimal number of engines tagged for maintenance on any aircraft in the fleet that arrives at the service facility. Since the materials, technicians and other costs are proprietary information, this study is time-based but allowance is made for the user to include associated costs and thus perform cost-based decision making.
Applied Science, Faculty of
Mechanical Engineering, Department of
Graduate

10

Sevinc, Suleyman. "Automatic simplification of models in a hierarchical, modular discrete event simulation environment." Diss., The University of Arizona, 1988. http://hdl.handle.net/10150/184409.

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This research is concerned with the simplification of discrete event simulation models. Specifically, the necessary concepts and tools are investigated for the automatic generation of simplified versions of a discrete event model. The simplification process developed utilizes observations of model behavior. It is based on Zeigler's DEVS formalism developed in his book "Multifacetted Modelling and Discrete Event Simulation". Therefore, the simplified models that are generated adhere to all the canons of this formalism. The implementation is designed as an extension to DEVS-Scheme, a hierarchical modular discrete event modelling and simulation environment. It consists of two layers: the lower layer observes the model behavior and the higher layer generates the code from the observation data obtained at the lower layer. The resulting system allows one to generate simplified models at any level of the model structure hierarchy. A local area network model base is developed and used to demonstrate the validity and complexity reduction of the simplified models relative to the models from which they are extracted.

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Книги з теми "Discrete-Event models":

1

Delaney, William. Dynamic models and discrete event simulation. New York: M. Dekker, 1989.

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2

Varaiya, Pravin, and Alexander B. Kurzhanski, eds. Discrete Event Systems: Models and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0042300.

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3

Leemis, Lawrence M. Discrete-event simulation: A first course. Upper Saddle River, N.J: Pearson Prentice Hall, 2006.

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4

Wainer, Gabriel A. Discrete-event modeling and simulation: A practitioner's approach. Boca Raton: CRC Press, 2009.

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5

Kumar, Ratnesh. Modeling and control of logical discrete event systems. Boston: Kluwer Academic Publishers, 1995.

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6

Kedem, Zvi M. Relational database behavior: utilizing relational discrete event systems and models. New York: Courant Institute of Mathematical Sciences, New York University, 1988.

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7

Yucesan, Enver. "Simulation graphs for design and analysis of discrete event simulation models". Fontainbleau: INSEAD, 1986.

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8

Yücesan, Enver. On the intractability of verifying structural properties of discrete event simulation models. Fontainebleau: INSEAD, 1992.

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9

Zhou, MengChu. Petri net synthesis for discrete event control of manufacturing systems. Boston: Kluwer Academic, 1993.

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10

R, Kumar P., and Varaiya P. P, eds. Discrete event systems, manufacturing systems, and communication networks. New York: Springer Verlag, 1995.

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Частини книг з теми "Discrete-Event models":

1

Guzzella, Lino, and Christopher H. Onder. "Discrete-Event Models." In Introduction to Modeling and Control of Internal Combustion Engine Systems, 147–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10775-7_3.

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2

Guzzella, Lino, and Christopher H. Onder. "Discrete-Event Models." In Introduction to Modeling and Control of Internal Combustion Engine Systems, 129–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08003-0_3.

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3

Raczynski, Stanislaw. "Discrete Event Models." In Models for Research and Understanding, 171–87. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11926-2_7.

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4

Mader, Angelika. "A Classification of PLC Models and Applications." In Discrete Event Systems, 239–46. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4493-7_24.

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5

Cassandras, Christos G., and Stéphane Lafortune. "Timed Models." In Introduction to Discrete Event Systems, 275–316. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-4070-7_5.

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6

Cassandras, Christos G., and Stéphane Lafortune. "Systems and Models." In Introduction to Discrete Event Systems, 1–59. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-4070-7_1.

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7

Cassandras, Christos G., and Stéphane Lafortune. "Systems and Models." In Introduction to Discrete Event Systems, 1–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72274-6_1.

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8

Cassandras, Christos G., and Stéphane Lafortune. "Timed and Hybrid Models." In Introduction to Discrete Event Systems, 303–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72274-6_5.

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9

Varaiya, Pravin. "Process Models for Discrete Event Systems." In Realization and Modelling in System Theory, 23–41. Boston, MA: Birkhäuser Boston, 1990. http://dx.doi.org/10.1007/978-1-4612-3462-3_3.

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10

Garrido, José M. "More Simulation Models." In Object-Oriented Discrete-Event Simulation with Java, 183–96. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1319-3_13.

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Тези доповідей конференцій з теми "Discrete-Event models":

1

"Observations of Discrete Event Models." In 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004054300320041.

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2

CANUTO, E. "DISCRETE-EVENT MODELS OF MANUFACTURING SYSTEMS." In Proceedings of the 6th IEEE Mediterranean Conference. WORLD SCIENTIFIC, 1999. http://dx.doi.org/10.1142/9789814447317_0124.

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3

Barbey, Hans-Peter. "Validation Of Unstable Discrete Event Models." In 25th Conference on Modelling and Simulation. ECMS, 2011. http://dx.doi.org/10.7148/2011-0569-0572.

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4

Seybold, Lothar, Piotr Witczak, and Pawel Majdzik. "Modeling discrete-event systems with constraints." In 2013 18th International Conference on Methods & Models in Automation & Robotics (MMAR). IEEE, 2013. http://dx.doi.org/10.1109/mmar.2013.6670013.

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5

Panayiotou, Christos G., and Christos G. Cassandras. "IPA for delay threshold violation using stochastic fluid models." In 2008 9th International Workshop on Discrete Event Systems. IEEE, 2008. http://dx.doi.org/10.1109/wodes.2008.4605939.

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6

Capra, Lorenzo. "Applying Structural Techniques for Efficient Analysis of Complex SWN Models." In Proceedings. Eighth International Workshop on Discrete Event Systems. IEEE, 2006. http://dx.doi.org/10.1109/wodes.2006.382529.

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7

"A generic approach to build plant models for DES verification purposes." In 2006 8th International Workshop on Discrete Event Systems. IEEE, 2006. http://dx.doi.org/10.1109/wodes.2006.382508.

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8

Trcka, N., S. Georgievska, J. Markovski, S. Andova, and E. P. de Vink. "Performance analysis of χ models using discrete-time probabilistic reward graphs." In 2008 9th International Workshop on Discrete Event Systems. IEEE, 2008. http://dx.doi.org/10.1109/wodes.2008.4605931.

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9

Raunak, Mohammad, and Megan Olsen. "Quantifying validation of discrete event simulation models." In 2014 Winter Simulation Conference - (WSC 2014). IEEE, 2014. http://dx.doi.org/10.1109/wsc.2014.7019927.

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10

Bisgambiglia, P. A., L. Capocchi, P. Bisgambiglia, and S. Garredu. "Fuzzy inference models for Discrete EVent systems." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584707.

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Звіти організацій з теми "Discrete-Event models":

1

Lynch, Nancy. I/O Automata: A Model for Discrete Event Systems. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada196047.

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2

Sprock, Timothy, and Conrad Bock. Model of Operational Control of Discrete Event Logistics Systems (DELS). National Institute of Standards and Technology, July 2021. http://dx.doi.org/10.6028/nist.ir.8378.

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3

Tarca, Silvio, Stephan Eidenbenz, and Lukas Kroc. EconoSim: discrete event simulations of an agent-based model of the U.S. economy. Office of Scientific and Technical Information (OSTI), July 2011. http://dx.doi.org/10.2172/1084579.

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4

Martindale, Michael. A Discrete-Event Simulation Model for Evaluating Air Force Reusable Military Launch Vehicle Post-Landing Operations. Fort Belvoir, VA: Defense Technical Information Center, June 2006. http://dx.doi.org/10.21236/ada457121.

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5

Kimball, Devin. Discrete-event Simulation Process Model for the Pyrochemical Processing of Plutonium at Los Alamos National Laboratory. Office of Scientific and Technical Information (OSTI), May 2022. http://dx.doi.org/10.2172/1868214.

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6

Wilke, Jeremiah J., and Joseph P. Kenny. Using Discrete Event Simulation for Programming Model Exploration at Extreme-Scale: Macroscale Components for the Structural Simulation Toolkit (SST). Office of Scientific and Technical Information (OSTI), February 2015. http://dx.doi.org/10.2172/1170619.

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