Table of Contents

• Addressing the challenges of aerospace and defense initiatives using software analysis toolsPaul L. Graziani. 3-4 [doi]
• Introduction to simulation: introduction to simulationRicki G. Ingalls. 7-16 [doi]
• Spreadsheet simulation: spreadsheet simulationAndrew F. Seila. 17-22 [doi]
• Output analysis: simulation output analysisMarvin K. Nakayama. 23-34 [doi]
• Input modeling: answers to the top ten input modeling questionsBahar Biller, Barry L. Nelson. 35-40 [doi]
• Simulation optimization: simulation-based optimizationAverill M. Law, Michael G. McComas. 41-44 [doi]
• Simulation experiments: designing simulation experimentsRussell R. Barton. 45-51 [doi]
• Verification validation: model verification and validationJohn S. Carson II. 52-58 [doi]
• Supply chain analysis: supply chain analysis: spreadsheet or simulation?Leonardo Chwif, Marcos Ribeiro Pereira Barretto, Eduardo Saliby. 59-66 [doi]
• Software evaluation and selection: an evaluation and selection methodology for discrete-event simulation softwareTamrat W. Tewoldeberhan, Alexander Verbraeck, Edwin Valentin, Gilles Bardonnet. 67-75 [doi]
• Simulation optimization: simulation optimizationSigurdur Ólafsson, Jumi Kim. 79-84 [doi]
• Statistical analysis of simulation output: output data analysis for simulationsChristos Alexopoulos, Seong-Hee Kim. 85-96 [doi]
• Inside simulation software: how it works and why it matters: inside discrete-event simulation software: how it works and why it mattersThomas J. Schriber, Daniel T. Brunner. 97-107 [doi]
• Adaptive monte carlo methods for rare event simulation: adaptive monte carlo methods for rare event simulationsMinghua Hsieh. 108-115 [doi]
• Exploring the world of agent-based simulations: simple models, complex analyses: exploring the world of agent-based simulations: simple models, complex analysesSusan M. Sanchez, Thomas W. Lucas. 116-126 [doi]
• Manufacturing modeling architectures: architectural concepts for a system simulator for concurrent prototypng of equipment and controlsK. Preston White, Ryan Fritz, Stephen Horvath, Carlos Orellana II, Jonathan Wohlers, Richard G. Fairbrother, William S. Terry. 117-122 [doi]
• Key requirements for cave simulations: key requirements for cave simulationsScott M. Preddy, Richard E. Nance. 127-135 [doi]
• Bayesian statistics and the monte carlo method: bayesian statistics and the monte carlo methodThomas N. Herzog. 136-146 [doi]
• Simulation-based engineering of complex systems: simulation-based engineering of complex systems using EXTEND+MFG+OpeMCSSJohn R. Clymer. 147-156 [doi]
• Human performance modeling for discrete-event simulation: human performance modeling for discrete-event simulation: workloadJohn Keller. 157-162 [doi]
• Arena: the arena product family: enterprise modeling solutionsJudy Rathmell, David T. Sturrock. 165-172 [doi]
• AutoMod: simulating reality using AutoModMatthew W. Rohrer, Ian McGregor. 173-181 [doi]
• Non-item based tools: non-item based discrete-event simulation toolsRichard A. Phelps, David J. Parsons, Andrew J. Siprelle. 182-186 [doi]
• Micro Saint: Micro Saint modeling and the human elementDaniel W. Schunk, Wendy K. Bloechle, K. Ronald Laughery Jr.. 187-191 [doi]
• Promodel: simulation modeling using promodel technologyCharles R. Harrell, Rochelle N. Price. 192-198 [doi]
• ExpertFit: how the ExpertFit distribution-fitting software can make your simulation models more validAverill M. Law, Michael G. McComas. 199-204 [doi]
• Extend: the Extend simulation environmentDavid Krahl. 205-213 [doi]
• Credibility assessment: a collaborative evaluation environment for credibility assessment of modeling and simulation applicationsOsman Balci, Robin J. Adams, David S. Myers, Richard E. Nance. 214-220 [doi]
• AweSim: introduction to AweSimJean J. O Reilly. 221-224 [doi]
• Silk: object-oriented simulation with Java, Silk, and open SML .NET languagesRichard A. Kilgore. 227-233 [doi]
• SSJ: SSJ: a framework for stochastic simulation in JavaPierre L Ecuyer, Lakhdar Meliani, Jean G. Vaucher. 234-242 [doi]
• Simkit: component based simulation modeling with SimkitArnold H. Buss. 243-249 [doi]
• Flexsim: Flexsim simulation environmentWilliam B. Nordgren. 250-252 [doi]
• Advanced input modeling: parameter estimation for ARTA processesBahar Biller, Barry L. Nelson. 255-262 [doi]
• Advanced input modeling: properties of the NORTA method in higher dimensionsSoumyadip Ghosh, Shane G. Henderson. 263-269 [doi]
• Advanced input modeling: the vine copula method for representing high dimensional dependent distributions: application to continuous belief netsDorota Kurowicka, Roger M. Cooke. 270-278 [doi]
• Optimization via simulation: two-stage NP method with inheritanceJumi Kim, Sigurdur Ólafsson. 279-284 [doi]
• Optimization via simulation: randomized-direction stochastic approximation algorithms using deterministic sequencesXiaoping Xiong, I-Jeng Wang, Michael C. Fu. 285-291 [doi]
• Optimization via simulation: a combined procedure for optimization via simulationJuta Pichitlamken, Barry L. Nelson. 292-300 [doi]
• Rare event simulation and combinatorial optimization using cross entropy: estimating buffer overflows in three stages using cross-entropyPieter-Tjerk de Boer, Dirk P. Kroese, Reuven Y. Rubinstein. 301-309 [doi]
• Rare event simulation and combinatorial optimization using cross entropy: estimation of rare event probabilities using cross-entropyTito Homem-de-Mello, Reuven Y. Rubinstein. 310-319 [doi]
• Rare event simulation and combinatorial optimization using cross entropy: sequence alignment by rare event simulationJonathan M. Keith, Dirk P. Kroese. 320-327 [doi]
• Output analysis: a comparison of output-analysis methods for simulations of processes with multiple regeneration sequencesJames M. Calvin, Marvin K. Nakayama. 328-335 [doi]
• Output analysis: ASAP2: an improved batch means procedure for simulation output analysisNatalie M. Steiger, Christos Alexopoulos, David Goldsman, Emily K. Lada, James R. Wilson, Faker Zouaoui. 336-344 [doi]
• Output analysis: on choosing a single criterion for confidence-interval proceduresBruce W. Schmeiser, Yingchieh Yeh. 345-352 [doi]
• Panel discussion on current issues in input modeling: panel on current issues in simulation input modelingRussell R. Barton, Stephen E. Chick, Russell C. H. Cheng, Shane G. Henderson, Averill M. Law, Bruce W. Schmeiser, Lawrence Leemis, Lee Schruben, James R. Wilson. 353-369 [doi]
• Recent advances in simulation optimization: confidence regions for stochastic approximation algorithmsMinghua Hsieh, Peter W. Glynn. 370-376 [doi]
• Recent advances in simulation optimization: response surface methodology revisitedEbru Angün, Jack P. C. Kleijnen, Dick den Hertog, Gül Gürkan. 377-383 [doi]
• Recent advances in simulation optimization: a conservative adjustment to the ETSS procedureE. Jack Chen. 384-391 [doi]
• Simulation input analysis: collecting data and estimating parameters for input distributionsMichael Freimer, Lee Schruben. 393-399 [doi]
• Simulation input analysis: joint criterion for factor identification and parameter estimationStephen E. Chick, Szu-Hui Ng. 400-406 [doi]
• Simulation input analysis: difficulties in simulating queues with Pareto serviceDonald Gross, John Shortle, Martin J. Fischer, Denise M. Bevilacqua Masi. 407-415 [doi]
• Difficult queuing simulation problems: rare-event simulation for infinite server queuesRoberto Szechtman, Peter W. Glynn. 416-423 [doi]
• Difficult queuing simulation problems: a balanced likelihood ratio approach for analyzing rare events in a tandem Jackson networkBruce Shultes. 424-432 [doi]
• Difficult queuing simulation problems: simulating M/G/1 queues with heavy-tailed serviceJohn C. Sees Jr., John Shortle. 433-438 [doi]
• New simulation output analysis techniques: a statistical process control approach for estimating the warm-up periodStewart Robinson. 439-446 [doi]
• New simulation output analysis techniques: two-phase quantile estimationE. Jack Chen. 447-455 [doi]
• New simulation output analysis techniques: a batch means procedure for mean value estimation of processes exhibiting long range dependenceAndrés Suárez-González, José C. López-Ardao, Cándido López-García, Miguel Rodríguez-Pérez, Manuel Fernández-Veiga, Maria Estrella Sousa Vieira. 456-464 [doi]
• Optimization and response surfaces: an optimization-based multi-resolution simulation methodologyDarren T. Drewry, Paul F. Reynolds Jr., William R. Emanuel. 467-475 [doi]
• Optimization and response surfaces: on the response surface methodology and designed experiments for computationally intensive distributed aerospace simulationsPaul J. Stewart, Peter J. Fleming, Sheena A. MacKenzie. 476-482 [doi]
• Optimization and response surfaces: Gaussian radial basis functions for simulation metamodelingMiyoung Shin, Robert G. Sargent, Amrit L. Goel. 483-488 [doi]
• Parallel and distributed systems: load sharing in heterogeneous distributed systemsHelen D. Karatza, Ralph C. Hilzer Jr.. 489-496 [doi]
• Parallel and distributed systems: UML based modeling of performance oriented parallel and distributed applicationsSabri Pllana, Thomas Fahringer. 497-505 [doi]
• Parallel and distributed systems: simulation analysis of RLC timers in UMTS systemsXiao Xu, Yi-Chiun Chen, Hua Xu, Eren Gonen, Peijuan Liu. 506-512 [doi]
• Virtual worlds: experiencing virtual factories of the futureTony Waller, John Ladbrook. 513-517 [doi]
• Virtual worlds: VRML clients linked through concurrent chatLee A. Belfore II, Sudheer Battula. 518-524 [doi]
• Virtual worlds: simulation based optimization in fishery managementFarhad Azadivar, Tu Hoang Truong, Kevin D. E. Stokesbury, Brian J. Rothschild. 525-531 [doi]
• Methods for special applications: incorporating biology into discrete event simulation models of organ allocationJennifer Kreke, Andrew J. Schaefer, Derek C. Angus, Cindy L. Bryce, Mark S. Roberts. 532-536 [doi]
• Methods for special applications: a recursive method for traffic management through a complex path networkMichael Norman. 537-541 [doi]
• Methods for special applications: Cell-DEVS quantization techniques in a fire spreading applicationAlexandre Muzy, Eric Innocenti, Antoine Aïello, Jean François Santucci, Gabriel Andrés Wainer. 542-549 [doi]
• Practice: an examination of implementation in extend, arena, and silkSid Redman, Sarah Law. 550-556 [doi]
• Practice: why initial conditions are importantBruce Gunn, Saeid Nahavandi II. 557-562 [doi]
• Practice: guidelines for designing simulation building blocksEdwin Valentin, Alexander Verbraeck. 563-571 [doi]
• Extensions: simulation prototypingIngolf Ståhl. 572-579 [doi]
• Extensions: an efficient importance sampling method for rare event simulation in large scale tandem networksLei Wei, Honghui Qi. 580-587 [doi]
• Extensions: performance analysis of real-time DEVS modelsEzequiel Glinsky, Gabriel Andrés Wainer. 588-594 [doi]
• XML-based modeling and simulation: meta-models are models tooHans Vangheluwe, Juan de Lara. 597-605 [doi]
• XML-based modeling and simulation: web service technologies and their synergy with simulationSenthilanand Chandrasekaran, Gregory A. Silver, John A. Miller, Jorge Cardoso, Amit P. Sheth. 606-615 [doi]
• XML-based modeling and simulation: using XML for simulation modelingPaul A. Fishwick. 616-622 [doi]
• Open source initiatives for simulation software: next generation simulation environments founded on open source software and XML-based standard interfacesThomas Wiedemann. 623-628 [doi]
• Open source initiatives for simulation software: multi-language, open-source modeling using the microsoft .NET architectureRichard A. Kilgore. 629-633 [doi]
• Open source initiatives for simulation software: a web-ready HiMASS: facilitating collaborative, reusable, and distributed modeling and execution of simulation models with XMLThorsten Daum, Robert G. Sargent. 634-640 [doi]
• Improving the model development process: model testing: is it only a special case of software testing?C. Michael Overstreet. 641-647 [doi]
• Improving the model development process: what use is model reuse: is there a crook at the end of the rainbow?Ray J. Paul, Simon J. E. Taylor. 648-652 [doi]
• Improving the model development process: expanding our horizons in verification, validation, and accreditation research and practiceOsman Balci, Richard E. Nance, James D. Arthur, William F. Ormsby. 653-663 [doi]
• Network modeling and simulation: on standardized network topologies for network researchGeorge F. Riley. 664-670 [doi]
• Network modeling and simulation: a motion environment for wireless communications systems simulationsNathan Smith, Trefor J. Delve. 671-678 [doi]
• Network modeling and simulation: a scalable simulator for TinyOS applicationsL. Felipe Perrone, David M. Nicol. 679-687 [doi]
• Panel discussion on distributed simulation and industry: potentials and pitfalls: distributed simulation and industry: potentials and pitfallsAgostino G. Bruzzone, Simon J. E. Taylor, Richard M. Fujimoto, Boon-Ping Gan, Steffen Straßburger, Ray J. Paul. 688-694 [doi]
• Parallel and Distributed Simulation: distributed spatio-temporal modeling and simulationThomas Schulze, Andreas Wytzisk, Ingo Simonis, Ulrich Raape. 695-703 [doi]
• Parallel and distributed simulation: managing external workload with BSP time warpMalcolm Yoke-Hean Low. 704-711 [doi]
• Parallel and distributed simulation: fast cell level ATM network simulationXiao Zhonge, Rob Simmonds, Brian Unger, John G. Cleary. 712-719 [doi]
• Modeling very large scale systems: one-to-one modeling and simulation of unbounded systems: experiences and lessonsRohyt V. Belani, Saumitra M. Das, David A. Fisher. 720-724 [doi]
• Modeling very large scale systems: using simulation modeling to assess rail track infrastructure in densely trafficked metropolitan areasMaged M. Dessouky, Quan Lu, Robert C. Leachman. 725-731 [doi]
• Modeling very large scale systems: building complex models with LEGOs (Listener Event Graph Objects)Arnold H. Buss, Paul J. Sanchez. 732-737 [doi]
• Methods and tools for aerospace operations modeling and simulation: new perspectives towards modeling depot MROFrank Boydstun, Mike Graul, Perakath C. Benjamin, Michael K. Painter. 738-746 [doi]
• Methods and tools for aerospace operations modeling and simulation: generic simulation models of reusable launch vehiclesMartin J. Steele, Mansooreh Mollaghasemi, Ghaith Rabadi, Grant R. Cates. 747-753 [doi]
• Methods and tools for aerospace operations modeling and simulation: modeling the space shuttleGrant R. Cates, Martin J. Steele, Mansooreh Mollaghasemi, Ghaith Rabadi. 754-762 [doi]
• Methods and tools for aerospace operations modeling and simulation: Toolkit for Enabling Adaptive Modeling and Simulation (TEAMS)Perakath C. Benjamin, Mike Graul, Madhav Erraguntla. 763-771 [doi]
• Reusing simulation components: simulation software and model reuse: a polemicMichael Pidd. 772-775 [doi]
• Reusing simulation components: cost: a component-oriented discrete event simulatorGilbert Chen, Boleslaw K. Szymanski. 776-782 [doi]
• Reusing simulation components: generalizing: is it possible to create all-purpose simulations?Glenn P. Rioux, Richard E. Nance. 783-790 [doi]
• Web-based simulation 1: D-SOL; a distributed Java based discrete event simulation architecturePeter H. M. Jacobs, Niels A. Lang, Alexander Verbraeck. 793-800 [doi]
• Web-based simulation 1: transaction cycle of agents and web-based gaming simulation for international emissions tradingHideyuki Mizuta, Yoshiki Yamagata. 801-806 [doi]
• Web-based simulation 1: web based simulation center: professional support for simulation projectsJames O. Henriksen, André Hanisch, Stefan Osterburg, Peter Lorenz, Thomas J. Schriber. 807-815 [doi]
• Web-based simulation 2: performance prediction of dynamic component substitutionsDhananjai Madhava Rao, Philip A. Wilsey. 816-824 [doi]
• Web-based simulation 2: configuration, simulation and animation of manufacturing systems via the internetTom-David Graupner, Hendrik Richter, Wilfried Sihn. 825-831 [doi]
• Web-based simulation 2: the ABELS system: designing an adaptable interface for linking simulationsG. Ayorkor Mills-Tettey, Greg Johnston, Linda F. Wilson, Joseph M. Kimpel, Bin Xie. 832-840 [doi]
• Web-based simulation 3: simulation web services with .Net technologiesRichard A. Kilgore. 841-846 [doi]
• Web-based simulation 3: re-introducing web-based simulationSteven W. Reichenthal. 847-852 [doi]
• Military keynote: military-based virtual systems engineeringKenneth Mark Bryden. 855-858 [doi]
• Campaign analysis: representation of historical events in a military campaign simulation modelJohn Herington, Andrew Lane, Noel Corrigan, John A. Golightly. 859-863 [doi]
• Campaign analysis: the sortie generation rate modelJames W. Harris Jr.. 864-868 [doi]
• Unmanned aerial vehicles: MultiUAV: a multiple UAV simulation for investigation of cooperative controlSteven J. Rasmussen, Phillip R. Chandler. 869-877 [doi]
• Unmanned aerial vehicles: modeling considerations for wide area search munition effectiveness analysisDavid R. Jacques. 878-886 [doi]
• Unmanned aerial vehicles: modeling signal latency effects using arena?Shane Dougherty, Raymond R. Hill, James T. Moore. 887-892 [doi]
• Logistics 1: closed-loop, simulation-based, systems engineering approach to life cycle management of defense systemsSean Connors, Julie Gauldin, Marshall Smith. 893-900 [doi]
• Logistics 1: simulation of transportation logisticsDavid Goldsman, Sebastien Pernet, Keebom Kang. 901-904 [doi]
• Logistics 1: an analysis of STOM (Ship To Objective Maneuver) in sea based logisticsKeebom Kang, Kenneth H. Doerr, Kyle A. Bryan, Gregorio Ameyugo. 905-910 [doi]
• Logistics 2: applications of simulation in logistics combat developmentsGregory H. Graves, Jeffrey L. Higgins. 911-916 [doi]
• Logistics 2: evaluation of army corps artillery ammunition supply systems via simulationIhsan Sabuncuoglu, Durdu Hakan Utku. 917-920 [doi]
• Logistics 2: using simulation to understand Interim Brigade Combat Team (IBCT) munitions logisticsTodd S. Bertulis, John O. Miller. 921-927 [doi]
• Agent modeling: models of defeatGary W. King, Brent Heeringa, David L. Westbrook, Joe Catalano, Paul R. Cohen. 928-934 [doi]
• Agent modeling: the use of agent-based models in military concept developmentJeffrey R. Cares. 935-939 [doi]
• Agent modeling: reducing training costs through integration of simulations, C4I systems, and expert systemsMark Whelan, John Loftus, David Perme, Richard Baldwin. 940-947 [doi]
• Weapon and communication systems: virtual life assessment of electronic hardware used in the Advanced Amphibious Assault Vehicle (AAAV)Ricky Valentin, Jeremy Cunningham, Michael D. Osterman, Abhijit Dasgupta II, Michael G. Pecht, Dinos Tsagos. 948-953 [doi]
• Weapon and communication systems: simulating crisis communicationsWilliam S. Murphy Jr., Mark A. Flournoy. 954-959 [doi]
• Weapon and communication systems: modeling the wireless network architecture of land warriorFernando Maymi, John R. Surdu, Andrew Hall, Ryan Beltramini. 960-966 [doi]
• Plebes, dogs, and robots: simulation analysis of the United States military academy reception dayDominik Nogic, Michael Nowatkowski. 967-974 [doi]
• Plebes, dogs, and robots: simulating without dataDean S. Hartley III. 975-980 [doi]
• Plebes, dogs, and robots: the Umbra simulation framework as applied to building HLA federatesEric J. Gottlieb, Michael J. McDonald, Fred J. Oppel, J. Brian Rigdon, Patrick G. Xavier. 981-989 [doi]
• Manufacturing 1: simulation-based analysis of a complex printed circuit board testing processJeffrey S. Smith, Yali Li, Jason Gjesvold. 993-998 [doi]
• Manufacturing 1: a simulation study of high power detonator production transitionJohnell Gonzales-Lujan, Robert J. Burnside, George Tompkins. 999-1006 [doi]
• Manufacturing 1: capacity analysis of multi-product, multi-resource biotech facility using discrete event simulationPrasad V. Saraph. 1007-1012 [doi]
• Manufacturing 2: shop scheduling using Tabu search and simulationDaniel A. Finke, Deborah J. Medeiros, Mark T. Traband. 1013-1017 [doi]
• Manufacturing 2: a simulation study of robotic welding system with parallel and serial processes in the metal fabrication industryCarl R. Williams, Peraset Chompuming. 1018-1025 [doi]
• Manufacturing 2: creation of a self adaptive simulation for Radex Heraklit industriesShabnam Tauböck, Christian Wartha, Michael Steiner II, Gerhard Pirkner, Felix Breitenecker. 1026-1029 [doi]
• Manufacturing 3: discrete event simulation in automotive final process systemVishvas Patel, James Ashby, John Ma. 1030-1034 [doi]
• Manufacturing 3: a simulation study of an automotive foundry plant manufacturing engine blocksSang D. Choi, Anil R. Kumar, Abdolazim Houshyar. 1035-1040 [doi]
• Manufacturing 3: manufacturing process modeling of Boeing 747 moving line conceptsRoberto F. Lu, Shankar Sundaram. 1041-1045 [doi]
• Transportation and material handling: complexities of AGV modelling in newspaper roll delivery systemDaniel J. Muller, Sarah M. Cardinal, Juergen Baumbach. 1046-1051 [doi]
• Transportation and material handling: solving logistics and transportation problems in a job shopKambiz Farahmand, Arun Balasubramanian. 1052-1059 [doi]
• Best modeling methods: virtual factory: highly interactive visualisation for manufacturingWolfgang Müller-Wittig, Reginald C. Jegathese, Meehae Song, Jochen Manfred Quick, Haibin Wang, Yongmin Zhong. 1061-1064 [doi]
• Best modeling methods: turn lost production into profit -- discrete event simulation applied on resetting performance in manufacturing systemsBjørn Johansson, Jürgen Kaiser. 1065-1072 [doi]
• Best modeling methods: documentation of discrete event simulation models for manufacturing system life cycle simulationJan Oscarsson, Matías Urenda Moris. 1073-1078 [doi]
• Productivity improvement: shifting bottleneck detectionChristoph Roser, Masaru Nakano, Minoru Tanaka. 1079-1086 [doi]
• Productivity improvement: throughput sensitivity analysis using a single simulationChristoph Roser, Masaru Nakano, Minoru Tanaka. 1087-1094 [doi]
• Productivity improvement: productivity improvement in the wood industry using simulation and artificial intelligenceFelipe F. Baesler, Milton Moraga, Francisco J. Ramis. 1095-1098 [doi]
• Manufacturing modeling architectures: manufacturing adapter of distributed simulation systems using HLAHironori Hibino, Yoshiyuki Yura, Yoshiro Fukuda, Keiji Mitsuyuki, Kiyoshi Kaneda. 1099-1107 [doi]
• Manufacturing modeling architectures: an architecture for a generic data-driven machine shop simulatorCharles McLean, Al Jones, Y. Tina Lee, Frank Riddick. 1108-1116 [doi]
• Manufacturing modeling methods: USE of GI/G/1 queuing approximations to set tactical parameters for the simulation of MRP systemsSilvanus T. Enns, Sangjin Choi. 1123-1129 [doi]
• Manufacturing modeling methods: virtual reality simulation of a mechanical assembly production lineDeogratias Kibira, Chuck McLean. 1130-1137 [doi]
• Manufacturing modeling methods: an approach and interface for building generic manufacturing kanban-systems modelsEdward J. Williams, Onur M. Ülgen, Chris DeWitt. 1138-1141 [doi]
• Simulation of manufacturing operations: optimum-seeking simulation in the design and control of manufacturing systems: experience with optquest for arenaPaul Rogers. 1142-1150 [doi]
• Simulation of manufacturing operations: optimization of operations in a steel wire manufacturing companyJai Thomas, Jayesh Todi, Asif Paranjpe. 1151-1156 [doi]
• Simulation of manufacturing operations: optimization of buffer sizes in assembly systems using intelligent techniquesFulya Altiparmak, Berna Dengiz, Akif A. Bulgak. 1157-1162 [doi]
• Manufacturing supply chain applications: modeling computer assembly operations for supply chain integrationSanjay Jain, Ngai Fong Choong, William G. K. Lee. 1165-1173 [doi]
• Manufacturing supply chain applications: ai-based optimization for fleet management in maritime logisticsAgostino G. Bruzzone, Alessandra Orsoni, Roberto Mosca, Roberto Revetria. 1174-1182 [doi]
• Manufacturing supply chain applications: investigation of influence of modeling fidelities on supply chain dynamicsJayendran Venkateswaran, Young Jun Son, Boonserm Kulvatunyou. 1183-1191 [doi]
• Transportation applications of simulation: simulation reduces airline misconnections: a case studySuna Hafizogullari, Prathi Chinnusamy, Cenk Tunasar. 1192-1198 [doi]
• Transportation applications of simulation: simulation building blocks for airport terminal modelingAlexander Verbraeck, Edwin Valentin. 1199-1206 [doi]
• Transportation applications of simulation: the application of distributed simulation in TOMAS: redesigning a complex transportation modelMark B. Duinkerken, Jaap A. Ottjes, Gabriël Lodewijks. 1207-1213 [doi]
• Transportation applications of simulation: traffic simulation application to plan real-time distribution routesOscar Franzese, Shirish S. Joshi. 1214-1218 [doi]
• Advanced aviation concepts via simulation: techniques to enhance performance of an existing aviation simulationDavid Carnes, Frederick Wieland. 1219-1225 [doi]
• Advanced aviation concepts via simulation: research flight simulation of future autonomous aircraft operationsMario S. V. Valenti Clari, Rob C. J. Ruigrok, Bart W. M. Heesbeen, Jaap Groeneweg. 1226-1234 [doi]
• Advanced aviation concepts via simulation: a simulation study to investigate runway capacity using TAAMMassoud Bazargan, Kenneth Fleming, Subramanian Prakash. 1235-1243 [doi]
• Advanced aviation concepts via simulation: decision support for advanced aviation conceptsLisa A. Schaefer, Leonard A. Wojcik, Thomas P. Berry, Craig R. Wanke. 1244-1250 [doi]
• Simulation applications in the automotive industry: simulation anywhere any time: web-based simulation implementation for evaluating order-to-delivery systems and processesSoundar R. T. Kumara, Yong-Han Lee, Kaizhi Tang, Chad Dodd, Jeffrey D. Tew, Shang-Tae Yee. 1251-1259 [doi]
• Simulation applications in the automotive industry: establishment of product offering and production leveling principles via supply chain simulation under order-to-delivery environmentShang-Tae Yee. 1260-1268 [doi]
• Simulation applications in the automotive industry: sequencing production on an assembly line using goal chasing and user defined algorithmArvind Mane, Saeid Nahavandi, Jingxin Zhang. 1269-1273 [doi]
• Warehousing and inventory management: a simulation tool to determine warehouse efficiencies and storage allocationsJoseph G. Macro, Reino E. Salmi. 1274-1281 [doi]
• Warehousing and inventory management: a simulation model to validate and evaluate the adequacy of an analytical expression for proper safety stock sizingEduardo Saggioro Garcia, Caio Fiuza Silva, Eduardo Saliby. 1282-1288 [doi]
• Warehousing and inventory management: integrating simulation modeling and equipment condition diagnostics for predictive maintenance strategies -a case studyLuis Rene Contreras, Chirag Modi, Arunkumar Pennathur. 1289-1296 [doi]
• Manufacturing supply chain applications 1: decision support tool - supply chainChristian Wartha, Momtchil Peev, Andrei Borshchev, Alexei Filippov. 1297-1301 [doi]
• Maufacturing supply chain applications 1: capacity and backlog management in queuing-based supply chainsEdward G. Anderson, Douglas J. Morrice. 1302-1305 [doi]
• Maufacturing supply chain applications 1: supply chain multi-objective simulation optimizationJeffrey A. Joines, Deepak Gupta, Mahmut Ali Gokce, Russell E. King, Michael G. Kay. 1306-1314 [doi]
• Manufacturing supply chain applications 2: logistic simulator for steel producing factoriesSteven C. Hamoen, Dirk-Jan Moens. 1315-1318 [doi]
• Manufacturing supply chain applications 2: development of distributed simulation model for the transporter entity in a supply chain processRichard J. Linn, Chin-Sheng Chen, Jorge A. Lozan. 1319-1326 [doi]
• Manufacturing supply chain applications 2: parameterization of fast and accurate simulations for complex supply networksBrett Marc Duarte, John W. Fowler, Kraig Knutson, Esma Senturk Gel, Dan Shunk. 1327-1336 [doi]
• Manufacturing supply chain applications 3: multi-agent simulation of purchasing activities in organizationsMark J. R. Ebben, Luitzen de Boer, Corina E. PopSitar. 1337-1344 [doi]
• Manufacturing supply chain applications 3: efficient simulations of supply chainsDieter Armbruster, Daniel Marthaler, Christian A. Ringhofer. 1345-1348 [doi]
• Manufacturing supply chain applications 3: simulation of the material transporting and loading process in Pedro de Valdivia mineGloria J. Giacaman, Rodrigo P. Medel, Jorge A. Tabilo. 1349-1355 [doi]
• Wafer fabrication: effects of metrology load port buffering in automated 300mm factoriesRobert Wright II, Marlin Shopbell, Kristin Rust, Silpa Sigireddy. 1359-1364 [doi]
• Wafer fabrication: 300mm wafer fabrication line simulation modelSameer T. Shikalgar, David Fronckowiak, Edward A. MacNair. 1365-1368 [doi]
• Wafer fabrication: realizing 300mm fab productivity improvements through integrated metrologyK. J. Stanley, Timothy D. Stanley, José Maia. 1369-1376 [doi]
• Material handling: operational modeling and simulation of an inter-bay AMHS in semiconductor wafer fabricationJesus A. Jimenez, Bosun Kim, John W. Fowler, Gerald T. Mackulak, You In Choung, Dong Jin Kim. 1377-1382 [doi]
• Material handling: data-based node penalties in a path-finding algorithm in an automated material handling systemMiki Fukunari, Srinivas Rajanna, Robert J. Gaskins, Mary Ellen Sparrow. 1383-1386 [doi]
• Material handling: simulating the transport and scheduling of priority lots in semiconductor factoriesChad D. DeJong, Scott P. Wu. 1387-1391 [doi]
• Scheduling and dispatching: a simulation study on release, synchronization, and dispatching in MEMS fabricationWang Lixin, Loo Hay Lee. 1392-1400 [doi]
• Scheduling and dispatching: some issues of the critical ratio dispatch rule in semiconductor manufacturingOliver Rose. 1401-1405 [doi]
• Scheduling and dispatching: a finite-capacity beam-search-algorithm for production scheduling in semiconductor manufacturingIlka Habenicht, Lars Mönch. 1406-1413 [doi]
• Modeling methodology: new approaches for simulation of wafer fabrication: the use of control variates and calibration metricsChanettre Rasmidatta, Shari Murray, John W. Fowler, Gerald T. Mackulak. 1414-1422 [doi]
• Modeling methodology: simulation based cause and effect analysis of cycle time and WIP in semiconductor wafer fabricationChao Qi, Tuck Keat Tang, Appa Iyer Sivakumar. 1423-1430 [doi]
• Modeling methodology: using simulation to understand capacity constraints and improve efficiency on process toolsManuel Aybar, Kishore Potti, Todd LeBaron. 1431-1435 [doi]
• Simulation of customer-focused business processes: Six Sigma and simulation, so what s the correlation?David M. Ferrin, David Muthler, Martin J. Miller. 1439-1443 [doi]
• Simulation of customer-focused business processes: a simulator to improve patient s service in a network of clinic laboratoriesFrancisco J. Ramis, Jorge L. Palma, Víctor F. Estrada, Gloria Coscolla. 1444-1447 [doi]
• Simulation of customer-focused business processes: simulation of restaurant operations using the restaurant modeling studioDavid M. Brann, Beth C. Kulick. 1448-1453 [doi]
• Simulation of customer-focused business processes: the role of modeling demand in process re-engineeringCraig V. Robertson, Shelly Shrader, David R. Pendergraft, Lisa M. Johnson, Kenneth S. Silbert. 1454-1458 [doi]
• Making simulation relevant in business: how-to simulation: when knowing what to do is not enoughFrancisco Pulgar-Vidal. 1459-1464 [doi]
• Making simulation relevant in business: integrated development of nonlinear process planning and simulation-based shop floor controlSambong Kim, Jungyoup Woo, Sungsik Park, Buhwan Jung, Hyunbo Cho. 1465-1468 [doi]
• Making simulation relevant in business: to pool or not to pool? the benefits of combining queuing and simulation Nico M. van Dijk. 1469-1472 [doi]
• Making simulation relevant in business: achieving higher levels of CMMI maturity using simulationMartin J. Miller, Francisco Pulgar-Vidal, David M. Ferrin. 1473-1478 [doi]
• Simulation in financial engineering: simulation in financial engineeringJeremy Staum. 1481-1492 [doi]
• Derivatives and credit risk: importance sampling for multimodal functions and application to pricing exotic optionsAthanassios N. Avramidis. 1493-1501 [doi]
• Derivatives and credit risk: enhanced quasi-monte carlo methods with dimension reductionJunichi Imai, Ken Seng Tan. 1502-1510 [doi]
• Derivatives and credit risk: credit risk modeling for catastrophic eventsTarja Joro, Paul Na. 1511-1514 [doi]
• Asset price modeling: a spot pricing framework to enable pricing and risk management of inter-domain assured bandwidth servicesMehdi Aboulfadl, Aparna Gupta, Ritesh Pradhan, Shivkumar Kalyanaraman. 1515-1523 [doi]
• Asset price modeling: modeling growth stocks (part II)Samuel C. Kou, Steven G. Kou. 1524-1529 [doi]
• Asset price modeling: decision aids for scheduling and hedging (DASH) in deregulated electricity markets: a stochastic programming approach to power portfolio optimizationSuvrajeet Sen, Yu Lihua, Talat Genc. 1530-1538 [doi]
• Crystal ball software tutorial: crystal ball professional introductory tutorialLawrence I. Goldman. 1539-1545 [doi]
• OptQuest software tutorial: portfolio optimization for capital investment projectsJay April, Fred Glover, James P. Kelly. 1546-1554 [doi]
• Problems in financial engineering: optimal active management feesJaksa Cvitanic, Lionel Martellini, Fernando Zapatero. 1555-1559 [doi]
• Problems in financial engineering: convergence of the stochastic mesh estimator for pricing American optionsAthanassios N. Avramidis, Heinrich Matzinger. 1560-1567 [doi]
• Problems in financial engineering: security price dynamics and simulation in financial engineeringStewart Mayhew. 1568-1574 [doi]
• Energy production and trading: using computer simulation to mitigate risk in electricity generation/consumption collaboration policiesThomas F. Brady. 1575-1577 [doi]
• Energy production and trading: batting average: a composite measure of risk for assessing product differentiation in a simulation modelDaniel M. Hamblin, Brian T. Ratchford. 1578-1587 [doi]
• Energy production and trading: discrete event simulation for the risk of development of an oil fieldCarlos Magno C. Jacinto. 1588-1592 [doi]
• Financial derivatives and real options: hedging beyond duration and convexityJian Chen, Michael C. Fu. 1593-1599 [doi]
• Financial derivatives and real options: effect of implementation time on real options valuationHarriet Black Nembhard, Leyuan Shi, Mehmet Aktan. 1600-1605 [doi]
• financial derivatives and real options: an empirical evaluation of sampling methods in risk analysis simulation: quasi-monte carlo, descriptive sampling, and Latin Hypercube samplingEduardo Saliby, Flavio Pacheco. 1606-1610 [doi]
• Simulation standards: a framework for standard modular simulationCharles McLean, Swee Leong. 1613-1620 [doi]
• Simulation standards: proposed standard processes for certification of modeling and simulation applicationsOsman Balci, Said D. Saadi. 1621-1627 [doi]
• Simulation standards: the simulation professional certification program: a status reportRalph V. Rogers, Vince Amico, Mark Yerkes. 1628-1632 [doi]
• Future of simulation software: the current and future status of simulation software (panel)Robert Diamond, James O. Henriksen, Claude Dennis Pegden, Tony Waller, Charles R. Harrell, William B. Nordgren, Matthew W. Rohrer, Averill M. Law. 1633-1640 [doi]
• What s virtually possible: what s virtually possible?Wayne J. Davis. 1641-1651 [doi]
• Supply chain opportunities: panel session: opportunities for simulation in supply chain managementJerry Banks, Sanjay Jain, Stephen J. Buckley, Peter Lendermann, Mani S. Manivannan. 1652-1658 [doi]
• Real-time control: the extended use of simulation in evaluating real-time control systems of AGVs and automated material handling systemsCorné Versteegt, Alexander Verbraeck. 1659-1666 [doi]
• Real-time control: implementation of a simulation-based control architecture for supply chain interactionsSreeram Ramakrishnan, Seungyub Lee, Richard A. Wysk. 1667-1674 [doi]
• Real-time control: the possible role of a backbone architecture in real-time control and emulationCsaba Attila Boer, Alexander Verbraeck, Hans P. M. Veeke. 1675-1682 [doi]
• Equipment interface: the relationship between simulation and emulationIan McGregor. 1683-1688 [doi]
• Equipment interface: improving the quality of controls and reducing costs for on-site adjustments with emulation: an example of emulation in baggage handlingWilliam Rengelink, Yvo A. Saanen. 1689-1694 [doi]
• CEPM 1: lean construction and simulationDaniel W. Halpin, Marc Kueckmann. 1697-1703 [doi]
• CEPM 1: optimal construction project planningSimaan M. AbouRizk, Yasser Mohamed. 1704-1708 [doi]
• CEPM 1: special purpose simulation modeling of tower cranesBradford J. A. Appleton, John Patra, Yasser Mohamed, Simaan M. AbouRizk. 1709-1715 [doi]
• CEPM 2: simulation-based analysis of handling inbound containers in a terminalSgouris P. Sgouridis, Demos C. Angelides. 1716-1724 [doi]
• CEPM 1: application of simulation models in airport facility designNaren Doshi, Robert Moriyama. 1725-1730 [doi]
• CEPM 1: simulation modeling and optimization of stockyard layouts for precast concrete productsRamesh Marasini, Nashwan Dawood. 1731-1736 [doi]
• CEPM 3: lead time reduction via pre-positioning of inventory in an industrial construction supply chainKenneth D. Walsh, Tobi A. Walsh, James C. Hershauer, Iris D. Tommelein, Anil Sawhney. 1737-1744 [doi]
• CEPM 3: contributors to lead time in construction supply chains: case of pipe supports used in power plantsRoberto J. Arbulu, Iris D. Tommelein, Kenneth D. Walsh, James C. Hershauer. 1745-1751 [doi]
• CEPM 3: process mapping of residential foundation slab construction processesHoward H. Bashford, Anil Sawhney, André Mund, Kenneth D. Walsh. 1752-1758 [doi]
• CEPM 4: evaluation of policies for the maintenance of bridges using discrete-event simulationSrinath Devulapalli, Julio C. Martínez, Jesus M. de la Garza. 1759-1764 [doi]
• CEPM 4: comparison of simulation-driven construction operations visualization and 4D CADVineet R. Kamat, Julio C. Martínez. 1765-1770 [doi]
• CEPM 4: optimization of the waste management for construction projects using simulationMala Chandrakanthi, Patrick Hettiaratchi, Bolívar Prado, Janaka Y. Ruwanpura. 1771-1777 [doi]
• CEPM 5: simulation based project selection decision analysis toolGary Powers, Janaka Y. Ruwanpura, Garry Dolhan, Martin Chu. 1778-1785 [doi]
• CEPM 5: project management - critical path method (CPM) and PERT simulated with processmodelLeRoy F. Simmons. 1786-1788 [doi]
• CEPM 1: selecting earthmoving equipment fleets using genetic algorithmsMohamed Marzouk, Osama Moselhi. 1789-1796 [doi]
• Advancing learning goals: an undergraduate systems engineering design project for using constructive and virtual simulation for an armed UAV designSuzanne Oldenburg DeLong, Paul West. 1799-1803 [doi]
• Advancing learning goals: Linda arouses a sleeping barberJohn H. Reynolds. 1804-1808 [doi]
• Advancing learning goals: using simulation to teach business processes design and improvementMarvin S. Seppanen, Sameer Kumar. 1809-1814 [doi]
• Panel discussion on using simulation to teach probability: words and deeds: panel: using simulation to teach probability, session 1: words, session 2: deedsMatthew Rosenshine, Russell R. Barton, David Goldsman, Lawrence Leemis, Barry L. Nelson. 1815-1822 [doi]
• Supply chain planning: the role of simulation in advanced planning and schedulingKenneth J. Musselman, Jean O Reilly, Steven Duket. 1825-1830 [doi]
• Supply chain planning: promise and problems of simulation technology in SCM domainSam Bansal. 1831-1837 [doi]
• Supply chain planning: using simulation to evaluate buffer adjustment methods in order promisingHank Grant, Scott Moses, David Goldsman. 1838-1845 [doi]
• Semiconductor manufacturing: ASAP applications of simulation modeling in a wafer fabKishore Potti, Amit K. Gupta II. 1846-1848 [doi]
• Semiconductor manufacturing: design, development and application of an object oriented simulation toolkit for real-time semiconductor manufacturing schedulingChin Soon Chong, Appa Iyer Sivakumar, Robert K. L. Gay. 1849-1856 [doi]
• Semiconductor manufacturing: using simulation-based scheduling to maximize demand fulfillment in a semiconductor assembly facilityJuergen Potoradi, Ong Siong Boon, Scott J. Mason, John W. Fowler, Michele E. Pfund. 1857-1861 [doi]
• Semiconductor manufacturing: simulation based multiobjective schedule optimization in semiconductor manufacturingAmit K. Gupta II, Appa Iyer Sivakumar. 1862-1870 [doi]
• Maintenance and repair: application of simulation and mean value analysis to a repair facility model for finding optimal staffing levelsGord Boyer, Neil Arnason. 1871-1879 [doi]
• Maintenance and repair: a comparison of three optimization methods for scheduling maintenance of high cost, long-lived capital assetsTerry M. Helm, Steve W. Painter, W. Robert Oakes. 1880-1884 [doi]
• Maintenance and repair: a simulation model for field service with condition-based maintenanceYiqing Lin, Arthur Hsu, Ravi Rajamani. 1885-1890 [doi]
• Scheduling & control: rolling horizon scheduling in large job shopsKristin A. Thoney, Jeffrey A. Joines, Padmanabhan Manninagarajan, Thom J. Hodgson. 1891-1896 [doi]
• Scheduling & control: shop floor scheduling with simulation based proactive decision supportAmit K. Gupta II, Appa Iyer Sivakumar, Sumit Sarawgi. 1897-1902 [doi]
• Scheduling & control: process accompanying simulation: a general approach for the continuous optimization of manufacturing schedules in electronics productionSebastian Werner, Gerald Weigert. 1903-1908 [doi]
• Schedule evaluation: simulation optimization for process scheduling through simulated annealingAlex Cave, Saeid Nahavandi, Abbas Z. Kouzani. 1909-1913 [doi]
• Schedule evaluation: simulation of JIT performance in a printing shopBen M. Patterson, Mustafa Özbayrak, Theopisti Papadopoulou. 1914-1921 [doi]
• Schedule evaluation: simulation study of Dreyer urgent care facilityBoon Aik Tan, Aldas Gubaras, Nipa Phojanamongkolkij. 1922-1927 [doi]
• General methodology 1: a robust simulation-based multicriteria optimization methodologyRaid Al-Aomar. 1931-1939 [doi]
• General methodology 1: a handbook for integrating discrete event simulation as an aid in conceptual design of manufacturing systemsMats Jägstam, Par Klingstam. 1940-1944 [doi]
• General methodology 1: optimising discrete event simulation models using a reinforcement learning agentDouglas C. Creighton, Saeid Nahavandi. 1945-1950 [doi]
• General methodology 2: a comparison of selective initialization bias elimination methodsJennifer R. Linton, Catherine M. Harmonosky. 1951-1957 [doi]
• General methodology 2: an efficient method for simulating fractional stable motionWei Biao Wu, George Michailidis, Danlu Zhang. 1958-1963 [doi]
• General methodology 2: enabling large scale and high definition simulation of natural systems with vector models and JDEVSJean-Baptiste Filippi, Paul Bisgambiglia. 1964-1970 [doi]
• General methodology 3: a parallel simulation framework for infrastructure modeling and analysisDonald D. Dudenhoeffer, May R. Permann, Elliot M. Sussman. 1971-1977 [doi]
• General methodology 3: global search strategies for simulation optimisationGeorge D. Magoulas, Tillal Eldabi, Ray J. Paul. 1978-1985 [doi]
• General methodology 3: a federation object coordinator for simulation based control and analysisSeungyub Lee, Sreeram Ramakrishnan, Richard A. Wysk. 1986-1994 [doi]
• General applications 1: a simulation architecture with distributed controllers for cell-based manufacturing systemsHansoo Kim, SugJe Sohn, Ying Wang, Tolga Tezcan, Leon F. McGinnis, Chen Zhou. 1995-2002 [doi]
• General applications 1: a highly efficient M/G/Infinity model for generating self-similar tracesMaria Estrella Sousa Vieira, Andrés Suárez-González, Cándido López-García, Manuel Fernández-Veiga, José C. López-Ardao. 2003-2010 [doi]
• General applications 1: soccer championship analysis using monte carlo simulationCaio Fiuza Silva, Eduardo Saggioro Garcia, Eduardo Saliby. 2011-2016 [doi]
• General applications 2: decision making of embedded I/O buffer sizes using the queueing simulation model for a shared-memory systemJui-Hua Li, JoAnne Holliday, George Fegan. 2017-2021 [doi]
• General applications 2: a general simulation environment for IP mobilityPeng Sun, Sam Yuan Sung. 2022-2030 [doi]
• General applications 2: simulation system modeling for mass customization manufacturingGuixiu Qiao, Charles McLean, Frank Riddick. 2031-2036 [doi]