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Journal articles on the topic 'Discrete-event simulations'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Greenberg, Albert G., Boris D. Lubachevsky, and Isi Mitrani. "Superfast parallel discrete event simulations." ACM Transactions on Modeling and Computer Simulation 6, no. 2 (1996): 107–36. http://dx.doi.org/10.1145/232807.232818.

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2

Giannasi, Frank, Philip Lovett, and Anthony N. Godwin. "Enhancing confidence in discrete event simulations." Computers in Industry 44, no. 2 (2001): 141–57. http://dx.doi.org/10.1016/s0166-3615(00)00084-1.

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3

Pollatschek, M. A. "A library for discrete event simulations." ACM SIGSMALL/PC Notes 19, no. 1 (1993): 3–15. http://dx.doi.org/10.1145/155742.155745.

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4

Pukite, Paul, and Luke Ludwig. "Generic discrete event simulations using DEGAS :." ACM SIGAda Ada Letters XXVII, no. 3 (2007): 27–40. http://dx.doi.org/10.1145/1315607.1315592.

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5

Pidd, M., and R. A. Cassel. "Using Java to Develop Discrete Event Simulations." Journal of the Operational Research Society 51, no. 4 (2000): 405. http://dx.doi.org/10.2307/254167.

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6

Deligonul, Z. Seyda. "Antithetic Bias Reduction for Discrete-Event Simulations." Journal of the Operational Research Society 38, no. 5 (1987): 431. http://dx.doi.org/10.2307/2582732.

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7

Deligönül, Z. Şeyda. "Antithetic Bias Reduction for Discrete-Event Simulations." Journal of the Operational Research Society 38, no. 5 (1987): 431–37. http://dx.doi.org/10.1057/jors.1987.71.

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8

Pidd, M., and R. A. Cassel. "Using Java to develop discrete event simulations." Journal of the Operational Research Society 51, no. 4 (2000): 405–12. http://dx.doi.org/10.1057/palgrave.jors.2600898.

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9

Moiseev, Alexander, Anton Demin, Vadim Dorofeev, and Vasily Sorokin. "Discrete-Event Approach to Simulation of Queueing Networks." Key Engineering Materials 685 (February 2016): 939–42. http://dx.doi.org/10.4028/www.scientific.net/kem.685.939.

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The paper is devoted to basic principles to develop software queueing networks simulations. A mathematical model and general scheme of the queueing network are presented in the paper. Main network components and behavior parameters are described. The application can simulate the networks of rather complex configuration. Software under construction uses a discrete-event approach for the simulation process. Basic algorithm of the simulation is also presented.

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10

Obermaier, Christina, Raphael Riebl, Ali H. Al-Bayatti, Sarmadullah Khan, and Christian Facchi. "Measuring the Realtime Capability of Parallel-Discrete-Event-Simulations." Electronics 10, no. 6 (2021): 636. http://dx.doi.org/10.3390/electronics10060636.

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Speeding up Discrete Event Simulations (DESs) is a broad research field. Promising Parallel Discrete Event Simulation (PDES) approaches with optimistic and conservative synchronisation schemes have emerged throughout the years. However, in the area of real-time simulation, PDESs are rarely considered. This is caused by the complex problem of fitting parallel executed DES models to a real-time clock. Hence, this paper gives an extensive review of existing conservative and optimistic synchronisation schemes for PDESs. It introduces a metric to compare their real-time capabilities to determine wh

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11

Xie, Xu, and Alexander Verbraeck. "A particle filter-based data assimilation framework for discrete event simulations." SIMULATION 95, no. 11 (2018): 1027–53. http://dx.doi.org/10.1177/0037549718798466.

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With the advent of new sensor technologies and communication solutions, the availability of data for discrete event systems has greatly increased. This motivates research on data assimilation for discrete event simulations that has not yet fully matured. This paper presents a particle filter-based data assimilation framework for discrete event simulations. The framework is formally defined based on the Discrete Event System Specification formalism. To effectively apply particle filtering in discrete event simulations, we introduce an interpolation operation that considers the elapsed time (i.e

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12

Heidelberger, Philip. "Discrete Event Simulations and Parallel Processing: Statistical Properties." SIAM Journal on Scientific and Statistical Computing 9, no. 6 (1988): 1114–32. http://dx.doi.org/10.1137/0909077.

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13

Hu, Xiaolin, and Peisheng Wu. "A Data Assimilation Framework for Discrete Event Simulations." ACM Transactions on Modeling and Computer Simulation 29, no. 3 (2019): 1–26. http://dx.doi.org/10.1145/3301502.

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14

Haas, P. J., and G. S. Shedler. "Stochastic Petri net representation of discrete event simulations." IEEE Transactions on Software Engineering 15, no. 4 (1989): 381–93. http://dx.doi.org/10.1109/32.16599.

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15

Bojan, Jovanovski, Minovski Robert, Voessner Siegfried, and Lichtenegger Gerald. "Combining system dynamics and discrete event simulations: Overview of hybrid simulation models." Istra?ivanja i projektovanja za privredu 10, no. 3 (2012): 135–42. http://dx.doi.org/10.5937/jaes10-2512.

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16

Feldman, Phillip, Adviti Muni, and Glen Swindle. "An optimal termination testing procedure for discrete event simulations." Mathematics and Computers in Simulation 44, no. 1 (1997): 81–98. http://dx.doi.org/10.1016/s0378-4754(97)00051-7.

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17

Nicol, D. M. "Analysis of synchronization in massively parallel discrete-event simulations." ACM SIGPLAN Notices 25, no. 3 (1990): 89–98. http://dx.doi.org/10.1145/99164.99174.

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18

Nicol, David M. "Performance bounds on parallel self-initiating discrete-event simulations." ACM Transactions on Modeling and Computer Simulation 1, no. 1 (1991): 24–50. http://dx.doi.org/10.1145/102810.102812.

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19

Sui, Zhiquan, Neil Harvey, and Shrideep Pallickara. "On the distributed orchestration of stochastic discrete event simulations." Concurrency and Computation: Practice and Experience 26, no. 11 (2013): 1889–907. http://dx.doi.org/10.1002/cpe.3121.

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20

Lubachevsky, Boris D. "Several unsolved problems in large-scale discrete event simulations." ACM SIGSIM Simulation Digest 23, no. 1 (1993): 60–67. http://dx.doi.org/10.1145/174134.158467.

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21

Su, Chun‐Lien, Chan‐Nan Lu, Yuh‐Tzong Lin, and King‐Chun Tu. "Discrete event simulations of an integrated trouble management system." Journal of the Chinese Institute of Engineers 22, no. 2 (1999): 231–39. http://dx.doi.org/10.1080/02533839.1999.9670460.

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22

Bizarro, Pedro, Luís M. Silva, and João Gabriel Silva. "JWarp: a Java library for parallel discrete-event simulations." Concurrency: Practice and Experience 10, no. 11-13 (1998): 999–1005. http://dx.doi.org/10.1002/(sici)1096-9128(199809/11)10:11/13<999::aid-cpe406>3.0.co;2-s.

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23

Kamrud, Alexander J., Douglas D. Hodson, Gilbert L. Peterson, and Brian G. Woolley. "Unified behavior framework in discrete event simulation systems." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 14, no. 4 (2017): 471–81. http://dx.doi.org/10.1177/1548512916683450.

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Intelligent agents provide simulations a means to add lifelike behavior in place of manned entities. When implemented, typically a single intelligent agent model (or approach to defining decision making), such as rule-based, behavior trees, neural networks, etc., is selected. This choice introduces restrictions into what behaviors agents can manifest, and can require significant testing in edge cases. This paper presents the incorporation and application of the Unified Behavior Framework (UBF) into the Advanced Framework for Simulation, Integration, and Modeling environment. The UBF provides t

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24

Giribone, Pier Giuseppe, and Roberto Revetria. "Certificate pricing using Discrete Event Simulations and System Dynamics theory." Risk Management Magazine 16, no. 2 (2021): 75–93. http://dx.doi.org/10.47473/2020rmm0092.

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The study proposes an innovative application of Discrete Event Simulations (DES) and System Dynamics (SD) theory to the pricing of a certain kind of certificates very popular among private investors and, more generally, in the context of wealth management. The paper shows how numerical simulation software mainly used in traditional engineering, such as industrial and mechanical engineering, can be successfully adapted to the risk analysis of structured financial products. The article can be divided into three macro-sections: in the first part a synthetic overview of the most widespread option

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25

Borges, Francisco, Albert Gutierrez-Milla, Remo Suppi, and Emilio Luque. "Optimal Run Length for Discrete-event Distributed Cluster-based Simulations." Procedia Computer Science 29 (2014): 73–83. http://dx.doi.org/10.1016/j.procs.2014.05.007.

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26

Korniss, G. "Suppressing Roughness of Virtual Times in Parallel Discrete-Event Simulations." Science 299, no. 5607 (2003): 677–79. http://dx.doi.org/10.1126/science.1079382.

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27

McHANEY, R. "Modelling battery constraints in discrete event automated guided vehicle simulations." International Journal of Production Research 33, no. 11 (1995): 3023–40. http://dx.doi.org/10.1080/00207549508904859.

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28

Nicol, David M. "The cost of conservative synchronization in parallel discrete event simulations." Journal of the ACM 40, no. 2 (1993): 304–33. http://dx.doi.org/10.1145/151261.151266.

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29

Hiller, James B., and Thomas C. Hartrum. "Conservative synchronization in object-oriented parallel battlefield discrete event simulations." ACM SIGSIM Simulation Digest 27, no. 1 (1997): 12–19. http://dx.doi.org/10.1145/268823.268896.

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30

Ricker, S. L., N. Sarkar, and K. Rudiet. "A discrete-event systems approach to modeling dextrous manipulation." Robotica 14, no. 5 (1996): 515–25. http://dx.doi.org/10.1017/s0263574700020002.

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SUMMARYTo perform dextrous manipulation efficiently, it is necessary to coordinate the interactions of many component processes. This paper investigates one approach to coordination: discrete-event systems. The applicability of discrete-event systems to the modeling of dextrous manipulation tasks is studied. Discrete-event control theory offers formal methods for determining whether a coordinator of the components can be generated. A representative dextrous manipulation task, the planar Grasp-Lift-Replace task of Howe and Cutkosky, is presented as a discrete-event process. The task is extended

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31

Kaabi, Mohamed Ghaith, Arnaud Tonnelier, and Dominique Martinez. "On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks." Neural Computation 23, no. 5 (2011): 1187–204. http://dx.doi.org/10.1162/neco_a_00112.

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In traditional event-driven strategies, spike timings are analytically given or calculated with arbitrary precision (up to machine precision). Exact computation is possible only for simplified neuron models, mainly the leaky integrate-and-fire model. In a recent paper, Zheng, Tonnelier, and Martinez ( 2009 ) introduced an approximate event-driven strategy, named voltage stepping, that allows the generic simulation of nonlinear spiking neurons. Promising results were achieved in the simulation of single quadratic integrate-and-fire neurons. Here, we assess the performance of voltage stepping in

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32

Neumann, W. P., and P. Medbo. "Integrating human factors into discrete event simulations of parallel flow strategies." Production Planning & Control 20, no. 1 (2009): 3–16. http://dx.doi.org/10.1080/09537280802601444.

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33

Pancerella, Carmen M., and Paul F. Reynolds. "Disseminating critical target-specific synchronization information in parallel discrete event simulations." ACM SIGSIM Simulation Digest 23, no. 1 (1993): 52–59. http://dx.doi.org/10.1145/174134.158466.

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34

Bagrodia, R. L., and Wen-Toh Liao. "Maisie: a language for the design of efficient discrete-event simulations." IEEE Transactions on Software Engineering 20, no. 4 (1994): 225–38. http://dx.doi.org/10.1109/32.277572.

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35

Bursi, Fabio, Andrea Ferrara, Andrea Grassi, and Chiara Ronzoni. "Simulating Continuous Time Production Flows in Food Industry by Means of Discrete Event Simulation." International Journal of Food Engineering 11, no. 1 (2015): 139–50. http://dx.doi.org/10.1515/ijfe-2014-0002.

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Abstract The paper presents a new framework for carrying out simulations of continuous-time stochastic processes by exploiting a discrete event approach. The application scope of this work mainly refers to industrial production processes executed on a continuous flow of material (e.g. food and beverage industry) as well as production processes working on discrete units but characterized by a high speed flow (e.g. automated packaging lines). The proposed model, developed adopting the Discrete EVent system Specification (DEVS) formalism, defines a single generalized base unit able to represent,

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36

da Silva, Paulo Salem, and Ana C. V. de Melo. "On-the-fly verification of discrete event simulations by means of simulation purposes: Extended version." SIMULATION 89, no. 8 (2013): 977–1008. http://dx.doi.org/10.1177/0037549713490439.

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37

Kons, Kalvis, Pedro La Hera, and Dan Bergström. "Modelling Dynamics of a Log-Yard through Discrete-Event Mathematics." Forests 11, no. 2 (2020): 155. http://dx.doi.org/10.3390/f11020155.

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This article deals with the topic of modelling the log-yard of one of our industry partners. To this end, our framework is based on discrete-events modelling (DEM), as consequence that many stages of the process run as a sequence of events. The sequence starts when trucks, trains or ships arrive loaded with logs to the log-yard. A machine unloads these logs and accumulates them in different storage areas. Consequently, a machine transports logs from these areas to the pulp mill, thus finishing the process. As using probability density functions is the core concept of DEM, the necessary process

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38

Jeschke, Matthias, Roland Ewald, Alfred Park, Richard Fujimoto, and Adelinde M. Uhrmacher. "A parallel and distributed discrete event approach for spatial cell-biological simulations." ACM SIGMETRICS Performance Evaluation Review 35, no. 4 (2008): 22–31. http://dx.doi.org/10.1145/1364644.1364652.

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39

Middlebrooks, Sam E. "Neural Net and Discrete Event Simulations of C2 Performance in Army TOCs." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 48, no. 3 (2004): 513–17. http://dx.doi.org/10.1177/154193120404800351.

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40

Degeling, Koen, Hendrik Koffijberg, Mira D. Franken, Miriam Koopman, and Maarten J. IJzerman. "Comparing Strategies for Modeling Competing Risks in Discrete-Event Simulations: A Simulation Study and Illustration in Colorectal Cancer." Medical Decision Making 39, no. 1 (2018): 57–73. http://dx.doi.org/10.1177/0272989x18814770.

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Background. Different strategies toward implementing competing risks in discrete-event simulation (DES) models are available. This study aims to provide recommendations regarding modeling approaches that can be defined based on these strategies by performing a quantitative comparison of alternative modeling approaches. Methods. Four modeling approaches were defined: 1) event-specific distribution (ESD), 2) event-specific probability and distribution (ESPD), 3) unimodal joint distribution and regression model (UDR), and 4) multimodal joint distribution and regression model (MDR). Each modeling

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41

Bieger, Joshua, Jadalaine Ferrer, Dillon Riedlinger, William Xu, and Jeffrey Demarest. "Simulating Army Rail Yard Operations at the Port of Bremerhaven." Industrial and Systems Engineering Review 6, no. 2 (2019): 95–100. http://dx.doi.org/10.37266/iser.2018v6i2.pp95-100.

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To maintain the United States military’s capability to deploy rapidly across the globe, logistical planning tools, simulations, and models enhance leaders’ decision making abilities. This research develops a discrete event model designed to simulate military operations within a railyard in order to support the Engineer Research and Development Center’s (ERDC) Planning Logistics Analysis Network System (PLANS). The research team chose the Port of Bremerhaven, Germany as a case study due to its relevance to current military operations, granting us access to timely data and stakeholders with rece

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42

EL AJALTOUNI, ELIE, MING ZHANG, AZZEDINE BOUKERCHE, and ROBSON EDUARDO DE GRANDE. "AN ADAPTIVE DYNAMIC LOAD BALANCING TECHNIQUE FOR GRID-BASED LARGE SCALE DISTRIBUTED SIMULATIONS." Journal of Interconnection Networks 10, no. 04 (2009): 391–419. http://dx.doi.org/10.1142/s0219265909002637.

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Dynamic load balancing is a key factor in achieving high performance for large scale distributed simulations on grid infrastructures. In a grid environment, the available resources and the simulation's computation and communication behavior may experience critical run-time imbalances. Consequently, an initial static partitioning should be combined with a dynamic load balancing scheme to ensure the high performance of the distributed simulation. In this paper, we propose a dynamic load balancing scheme for distributed simulations on a grid infrastructure. Our scheme is composed of an online net

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43

Deniz, Fatih, M. Nedim Alpdemir, Ahmet Kara, and Halit Oğuztüzün. "Supporting dynamic simulations with Simulation Modeling Architecture (SiMA): a Discrete Event System Specification-based modeling and simulation framework." SIMULATION 88, no. 6 (2012): 707–30. http://dx.doi.org/10.1177/0037549711428233.

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44

Hu, Xiaolin, Yi Sun, and Lewis Ntaimo. "DEVS-FIRE: design and application of formal discrete event wildfire spread and suppression models." SIMULATION 88, no. 3 (2011): 259–79. http://dx.doi.org/10.1177/0037549711414592.

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DEVS-FIRE is a discrete event system specification (DEVS) model for simulating wildfire spread and suppression. It employs a cellular space model to simulate fire spread and agent models that interact with the cellular space to simulate fire suppression with realistic tactics. The complex interplay among forest cells and agents calls for formal treatment of the fire spread and fire suppression models to verify the correctness of DEVS-FIRE. This paper gives formal design specifications of fire spread and suppression agent models used in DEVS-FIRE and applies DEVS-FIRE to both artificially gener

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45

Sui, Zhiquan, Matthew Malensek, Neil Harvey, and Shrideep Pallickara. "Autonomous Orchestration of Distributed Discrete Event Simulations in the Presence of Resource Uncertainty." ACM Transactions on Autonomous and Adaptive Systems 10, no. 3 (2015): 1–20. http://dx.doi.org/10.1145/2746345.

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46

Zhang, Tiequan, Woo Tae Kim, and Russell Schwartz. "Investigating Scaling Effects on Virus Capsid-Like Self-Assembly Using Discrete Event Simulations." IEEE Transactions on NanoBioscience 6, no. 3 (2007): 235–41. http://dx.doi.org/10.1109/tnb.2007.903484.

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47

Eldredge, David L., John D. McGregor, and Marguerite K. Summers. "Applying the object-oriented paradigm to discrete event simulations using the C++ language." SIMULATION 54, no. 2 (1990): 83–91. http://dx.doi.org/10.1177/003754979005400205.

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48

Cronqvist, Mattias Lantz, Carl-Oscar Jonson, and Erik Prytz. "Development and Initial Validation of a Stochastic Discrete Event Simulation to Assess Disaster Preparedness." Prehospital and Disaster Medicine 34, s1 (2019): s118. http://dx.doi.org/10.1017/s1049023x19002528.

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Introduction:Assessing disaster preparedness in a given region is a complex problem. Current methods are often resource-intensive and may lack generalizability beyond a specific scenario. Computer-based stochastic simulations may be an additional method but would require systems that are valid, flexible, and easy to use. Emergo Train System (ETS) is an analog simulation system used for disaster preparedness assessments.Aim:To digitalize the ETS model and develop stochastic simulation software for improved disaster preparedness assessments.Methods:A simulation software was developed in C#. The

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49

Zhang, Xiange, Stefan K. Lhachimi, and Wolf H. Rogowski. "Reporting Quality of Discrete Event Simulations in Healthcare—Results From a Generic Reporting Checklist." Value in Health 23, no. 4 (2020): 506–14. http://dx.doi.org/10.1016/j.jval.2020.01.005.

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50

Ziganurova, L., M. A. Novotny, and L. N. Shchur. "Model for the evolution of the time profile in optimistic parallel discrete event simulations." Journal of Physics: Conference Series 681 (February 3, 2016): 012047. http://dx.doi.org/10.1088/1742-6596/681/1/012047.

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