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Journal articles on the topic 'Discrete-Event models'

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Published: 1 February 2023

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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.
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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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11

Kuljis, Jasna. "User interfaces and discrete event simulation models." Simulation Practice and Theory 1, no. 5 (May 1994): 207–21. http://dx.doi.org/10.1016/0928-4869(94)90011-6.

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12

Xie, Yu. "Log-Multiplicative Models for Discrete-Time, Discrete-Covariate Event-History Data." Sociological Methodology 24 (1994): 301. http://dx.doi.org/10.2307/270986.

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13

Vicino, Damian, Gabriel A. Wainer, and Olivier Dalle. "Uncertainty on Discrete-Event System Simulation." ACM Transactions on Modeling and Computer Simulation 32, no. 1 (January 31, 2022): 1–27. http://dx.doi.org/10.1145/3466169.

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Uncertainty Propagation methods are well-established when used in modeling and simulation formalisms like differential equations. Nevertheless, until now there are no methods for Discrete-Dynamic Systems. Uncertainty-Aware Discrete-Event System Specification (UA-DEVS) is a formalism for modeling Discrete-Event Dynamic Systems that include uncertainty quantification in messages, states, and event times. UA-DEVS models provide a theoretical framework to describe the models’ uncertainty and their properties. As UA-DEVS models can include continuous variables and non-computable functions, their simulation could be non-computable. For this reason, we also introduce Interval-Approximated Discrete-Event System Specification (IA-DEVS), a formalism that approximates UA-DEVS models using a set of order and bounding functions to obtain a computable model. The computable model approximation produces a tree of all trajectories that can be traversed from the original model and some erroneous ones introduced by the approximation process. We also introduce abstract simulation algorithms for IA-DEVS, present a case study of UA-DEVS, its IA-DEVS approximation and, its simulation results using the algorithms defined.
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14

Raczynski, Stanislaw. "Simultaneous events, singularity in the space of models and chicken game." International Journal of Modeling, Simulation, and Scientific Computing 12, no. 04 (May 18, 2021): 2140002. http://dx.doi.org/10.1142/s179396232140002x.

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The discrete event simulation with simultaneous events is discussed. Discrete models are treated as some points in the general space of models. The main topic of this paper is the convergence of sequences of continuous models to the corresponding discrete event model. It is pointed out that the discrete event models may represent some singularities in the space of models. This fact is related to the problem of validity of discrete event simulation. The models presented here are not the topic of the paper. These are known models, used as examples. The model of the “chicken game” and multiple elastic collisions are considered. It is shown that if a discrete event model is some special limit case of a sequence of continuous models with finite event duration, then that limit model is a singularity. It may provide results very different from the expected limit value of the results from the converging sequence of models.
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15

Flitman, A. M., and R. D. Hurrion. "Linking Discrete-Event Simulation Models with Expert Systems." Journal of the Operational Research Society 38, no. 8 (August 1987): 723. http://dx.doi.org/10.2307/2582844.

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16

Kemper, P., and C. Tepper. "Automated Trace Analysis of Discrete-Event System Models." IEEE Transactions on Software Engineering 35, no. 2 (March 2009): 195–208. http://dx.doi.org/10.1109/tse.2008.75.

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17

Muzy, Alexandre, Eric Innocenti, Antoine Aiello, Jean-François Santucci, and Gabriel Wainer. "Specification of Discrete Event Models for Fire Spreading." SIMULATION 81, no. 2 (February 2005): 103–17. http://dx.doi.org/10.1177/0037549705052230.

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18

McKenzie, F. Ellis, Roger C. Wong, and William H. Bossert. "Discrete-Event Simulation Models of Plasmodium falciparum Malaria." SIMULATION 71, no. 4 (October 1998): 250–61. http://dx.doi.org/10.1177/003754979807100405.

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19

Flitman, A. M., and R. D. Hurrion. "Linking Discrete-Event Simulation Models with Expert Systems." Journal of the Operational Research Society 38, no. 8 (August 1987): 723–33. http://dx.doi.org/10.1057/jors.1987.121.

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20

FÖrstner, Dirk, and Jan Lunze. "Discrete-event models of quantized systems for diagnosis." International Journal of Control 74, no. 7 (January 2001): 690–700. http://dx.doi.org/10.1080/00207170010025276.

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21

de Swaan Arons, Henk, and Csaba Attila Boer. "Storage and retrieval of discrete-event simulation models." Simulation Practice and Theory 8, no. 8 (July 2001): 555–76. http://dx.doi.org/10.1016/s0928-4869(01)00036-2.

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22

Correcher, A., E. García, F. Morant, and E. Quiles. "Intermittent failure diagnosis based on discrete event models." IFAC Proceedings Volumes 37, no. 18 (September 2004): 147–52. http://dx.doi.org/10.1016/s1474-6670(17)30737-1.

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23

Moreira, Marcos V., and Jean-Jacques Lesage. "Fault diagnosis based on identified discrete-event models." Control Engineering Practice 91 (October 2019): 104101. http://dx.doi.org/10.1016/j.conengprac.2019.07.019.

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24

Turner, Stephen J. "Models of computation for parallel discrete event simulation." Journal of Systems Architecture 44, no. 6-7 (March 1998): 395–409. http://dx.doi.org/10.1016/s1383-7621(97)00055-6.

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25

Inan, K., and P. Varaiya. "Finitely recursive process models for discrete event systems." IEEE Transactions on Automatic Control 33, no. 7 (July 1988): 626–39. http://dx.doi.org/10.1109/9.1271.

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26

PEREIRA REMELHE, MANUEL A., and SEBASTIAN ENGELL. "COMBINING MODELICA MODELS WITH DISCRETE EVENT FORMALISMS FOR SIMULATION USING THE DES/M ENVIRONMENT." International Journal of Software Engineering and Knowledge Engineering 15, no. 02 (April 2005): 349–55. http://dx.doi.org/10.1142/s0218194005001999.

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Technical systems that include complex physical dynamics as well as extensive discrete event control, require powerful modeling and simulation techniques. As the most adequate means for modeling hybrid physical systems, we advocate the use of object-oriented modeling languages such as Modelica. However, the discrete event models often require the use of dedicated graphical editors that cannot be defined appropriately using Modelica. The purpose of the DES/M modeling environment [10] is to provide such editors for different discrete event formalisms and to translate discrete event models automatically into Modelica components such that a discrete event controller can be integrated easily into Modelica models and simulated using standard Modelica software tools. This contribution presents the main concepts used for the representation of several discrete event formalisms in the Modelica language and discusses the class of discrete event formalisms that can be supported by the DES/M environment.
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27

XIA, Wei, Yi-Ping YAO, Xiao-Dong MU, and Lin LIU. "Parallel Model Checking for Discrete Event Simulation Models Based on Event Graphs." Journal of Software 23, no. 6 (August 29, 2012): 1429–43. http://dx.doi.org/10.3724/sp.j.1001.2012.04047.

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28

FITZGERALD, J. S., P. G. LARSEN, K. G. PIERCE, and M. H. G. VERHOEF. "A formal approach to collaborative modelling and co-simulation for embedded systems." Mathematical Structures in Computer Science 23, no. 4 (July 8, 2013): 726–50. http://dx.doi.org/10.1017/s0960129512000242.

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The effective use of model-based formal methods in the development of complex embedded systems requires the integration of discrete-event models of controllers with continuous-time models of their environments. This paper proposes a new approach to the development of such combined models (co-models), in which an initial discrete-event model may include approximations of continuous-time behaviour that can subsequently be replaced by couplings to continuous-time models. An operational semantics of co-simulation allows the discrete and continuous models to run on their respective simulators and managed by a coordinating co-simulation engine. This permits the exploration of the composite co-model's behaviour in a range of operational scenarios. The approach has been realised using the Vienna Development Method (VDM) as the discrete-event formalism, and 20-sim as the continuous-time framework, and has been applied successfully to a case study based on the distributed controller for a personal transporter device.
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29

Letier, Emmanuel, Jeff Kramer, Jeff Magee, and Sebastian Uchitel. "Fluent temporal logic for discrete-time event-based models." ACM SIGSOFT Software Engineering Notes 30, no. 5 (September 2005): 70–79. http://dx.doi.org/10.1145/1095430.1081719.

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30

Hooper, James W. "Strategy-related characteristics of discrete-event languages and models." SIMULATION 46, no. 4 (April 1986): 153–59. http://dx.doi.org/10.1177/003754978604600403.

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31

McKenzie, F. Ellis, Roger C. Wong, and William H. Bossert. "Discrete-Event Models of Mixed-Phenotype Plasmodium falciparum Malaria." SIMULATION 73, no. 4 (October 1999): 213–17. http://dx.doi.org/10.1177/003754979907300403.

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32

Philips, P. P. H. H., W. P. M. H. Heemels, H. A. Preisig, and P. P. J. Van Den Bosch. "Control of quantized systems based on discrete event models." International Journal of Control 76, no. 3 (January 2003): 277–94. http://dx.doi.org/10.1080/0020717031000067402.

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33

Frough, Omid, Anuradha Khetwal, and Jamal Rostami. "Predicting TBM utilization factor using discrete event simulation models." Tunnelling and Underground Space Technology 87 (May 2019): 91–99. http://dx.doi.org/10.1016/j.tust.2019.01.017.

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34

Fanni, A., and A. Giua. "Discrete event representation of qualitative models using Petri nets." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 28, no. 6 (1998): 770–80. http://dx.doi.org/10.1109/3477.735387.

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35

Hu, Qiying, and Wuyi Yue. "Two new optimal models for controlling discrete event systems." Journal of Industrial & Management Optimization 1, no. 1 (2005): 65–80. http://dx.doi.org/10.3934/jimo.2005.1.65.

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36

Kim, J., and N. Fard. "Discrete-Event Simulation Of Network Reliability And Markovian Models." International Journal of Modelling and Simulation 15, no. 2 (January 1995): 65–71. http://dx.doi.org/10.1080/02286203.1995.11760253.

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37

Jacob, Romain, Jean-Jacques Lesage, and Jean-Marc Faure. "Opacity of Discrete Event Systems: models, validation and quantification." IFAC-PapersOnLine 48, no. 7 (2015): 174–81. http://dx.doi.org/10.1016/j.ifacol.2015.06.490.

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38

Patra, Amit, Siddhartha Mukhopadhyay, and Supratik Bose. "Logical models of discrete event systems: A comparative exposition." Sadhana 21, no. 6 (December 1996): 683–718. http://dx.doi.org/10.1007/bf02745366.

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39

Da Silveira, Marcos R., and Michel Combacau. "Supervision and control of heterarchical discrete event systems." Sba: Controle & Automação Sociedade Brasileira de Automatica 17, no. 1 (March 2006): 1–9. http://dx.doi.org/10.1590/s0103-17592006000100001.

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This paper presents a method for distributing centralized control models of discrete events systems (DES). The contribution of our approach is to offer a systematic way to decompose centralised models in order to obtain a modular representation of the production process. We observe as an advantage of this method that the main model properties are preserved and that the knowledge acquired during the modelling process is still valuable for the new structure. We emphasise that redundant information can be introduced to the system to increase local autonomy and to help the local fault detection and identification functions. Another important aspect is that the distributed models (sub-models) can be modified in order to increase their flexibility and to reach a more realistic behaviour representation. A set of terminologies and definitions related to the Control and Supervision domain are introduced in this paper to assist the understanding of our work.
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40

Suharko, Arief. "A Capacity Planning through Discrete Event Simulation." Jurnal PASTI 14, no. 2 (November 9, 2020): 146. http://dx.doi.org/10.22441/pasti.2020.v14i2.005.

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The capacity planning serves an important role in strategic decisions involving production facilities. While there are many publications made on capacity planning, most of the models created tend to restrict their applications in real-world due to some initial assumptions being made and/or the run-time execution of the models that may be prohibitive. The objective of this paper is to explore the model construction for in-plant truck movement in a cement company that is based on building a discrete-event simulation one so that the planning may be sufficiently robust while the amount of time for constructing the model and the run-time still serve practical purposes. The model then is used to examine the effects of shifting bottlenecking and thus, allows users to identify critical resources for the production process. The results show that such a model provides the directions and aids for the management to make the strategic decisions.
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41

Doja, Albert, Laurent Capocchi, and Jean-François Santucci. "Computational challenges to test and revitalize Claude Lévi-Strauss transformational methodology." Big Data & Society 8, no. 2 (July 2021): 205395172110378. http://dx.doi.org/10.1177/20539517211037862.

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The ambition and proposal for data modeling of myths presented in this paper is to link contemporary technical affordances to some canonical projects developed in structural anthropology. To articulate the theoretical promise and innovation of this proposal, we present a discrete-event system specification modeling and simulation approach in order to perform a generative analysis and a dynamic visualization of selected narratives, aimed at validating and revitalizing the transformational and morphodynamic theory and methodology proposed by Claude Lévi-Strauss in his structural analysis of myth. After an analysis of Lévi-Strauss’s transformational methodology, we describe in detail how discrete-event system specification models are implemented and developed in the framework of a DEVSimPy software environment. The validation of the method involves a discrete-event system specification simulation based on the extension of discrete-event system specification models dedicated to provide a dynamic Google Earth visualization of the selected myth. Future work around the discrete-event system specification formalism in anthropology is described as well as future applications regarding the impact of computational models (discrete-event system specification formalism, Bayesian inferences, and object-oriented features) to new contemporary anthropological domains.
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42

HU, Xiaohua, Akihiro KANESHIGE, Katsuaki ONOGI, and Yoshiyuki NISHIMURA. "Algebraic Specification of Discrete Event System Models and Their Refinements." Transactions of the Society of Instrument and Control Engineers 30, no. 1 (1994): 79–87. http://dx.doi.org/10.9746/sicetr1965.30.79.

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43

Yago, CM, and FJ Diez. "EE13 Converting Discrete Event Simulation Networks (DESNETS) into Dice Models." Value in Health 25, no. 7 (July 2022): S337. http://dx.doi.org/10.1016/j.jval.2022.04.265.

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44

Whiteford, Tennille M., and Raymond Kwong. "PROBABILISTIC FAULT DIAGNOSIS IN DISCRETE EVENT SYSTEMS WITH INCOMPLETE MODELS." IFAC Proceedings Volumes 40, no. 6 (2007): 97–102. http://dx.doi.org/10.3182/20070613-3-fr-4909.00019.

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45

Saliby, Eduardo, and Ray J. Paul. "Implementing Descriptive Sampling in Three-Phase Discrete Event Simulation Models." Journal of the Operational Research Society 44, no. 2 (February 1993): 147. http://dx.doi.org/10.2307/2584363.

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46

Sánchez, Álvaro García, Miguel Ortega-Mier, and Roberto Arranz. "Discrete-Event Simulation Models for Assessing Incidents in Railway Systems." International Journal of Information Systems and Supply Chain Management 4, no. 2 (April 2011): 1–14. http://dx.doi.org/10.4018/jisscm.2011040101.

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This paper presents a discrete event simulation model developed with a commercial environment. A modular approach is adopted, which facilitates building models for different railway systems. A key feature of this simulator is that it simultaneously models train movements and passenger behavior. The simulator has been used to assess two different policies when short incidents occur. Incidents are characterized by different factors, which are analyzed for both policies. A case-study is presented based on a subsystem of the commuter train network of the province of Madrid in Spain.
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47

Kotevski, Zoran. "SIMULATING FSPN MODELS USING PROCESS-BASED DISCRETE -EVENT SIMULATION LANGUAGE." Acta Simulatio 4, no. 2 (June 30, 2018): 1–11. http://dx.doi.org/10.22306/asim.v4i2.42.

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48

Shchur, L. N., and L. V. Shchur. "Relation of Parallel Discrete Event Simulation algorithms with physical models." Journal of Physics: Conference Series 640 (September 28, 2015): 012065. http://dx.doi.org/10.1088/1742-6596/640/1/012065.

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49

Kwong, Raymond H., and David L. Yonge-Mallo. "Fault Diagnosis in Discrete-Event Systems: Incomplete Models and Learning." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41, no. 1 (February 2011): 118–30. http://dx.doi.org/10.1109/tsmcb.2010.2047257.

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50

Saliby, Eduardo, and Ray J. Paul. "Implementing Descriptive Sampling in Three-Phase Discrete Event Simulation Models." Journal of the Operational Research Society 44, no. 2 (February 1993): 147–60. http://dx.doi.org/10.1057/jors.1993.27.

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