Ñòàòüè 2008 ãîäà (A...Z)
Aguirre Adrián, Müller Enrique, Seffino Sebastián, Méndez Carlos A. Applying a simulation-based tool to productivity management in an automotive-parts industry // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1838-1846.
This work presents the development and application of an advanced modeling, simulation and optimization-based framework focused on the production process of a basic element of a internal combustion engine which is supplied by a leading factory in the Latin-American market. Lying on the concepts of the process-interaction approach, the principal components available in the discrete event simulation environment SIMUL8 were used to achieve the best representation of this complex manufacturing system.
Ahad Ali, Xiaohui Chen, Ziming Yang, Jay Lee, Jun Ni Optimized maintenance design for manufacturing performance improvement using simulation // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1811-1819.
This research presents optimized maintenance design using simulation to analyze the capability of auto part manufacturing production system. The integration of simulation and optimization is used to identify critical stations, an optimal system design and maintenance scheduling scheme sand evaluates their effects on the overall system performance. Most emphasis is focused on the impact on system by individual station reliability and the fluctuation of maintenance availability. The proposed simulation and optimization for maintenance design is validated through real-life application. This simulation modeling and optimization could help for manufacturing performance improvement.
Aksyonov K.A., Bykov E.A., Smoliy E.F., Khrenov A.A. Industrial Enterprises Business Processes Simulation with BPsim.MAS // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10, 2008. Miami, USA, 2008. p.1669- 1677.
Èìèòàöèîííîå ìîäåëèðîâàíèå áèçíåñ-ïðîöåññîâ ïðåäïðèÿòèé â BPsim.MAS.
Aksyonov K.A., Smoliy E.F., Khrenov A.A., Bykov E.A., Kolosov D.M. Development of Distributed Multi-agent Resource Conversion Processes based Simulation System BPsim.MAS // Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2008). Singapore: 2008. p.3624–3629.
Ðàçðàáîòêà ðàñïðåäåëåííîé ñèñòåìû èìèòàöèîííîãî ìîäåëèðîâàíèÿ ìóëüòèàãåíòíûõ ïðîöåññîâ ïðåîáðàçîâàíèÿ ðåñóðñîâ BPsim.MAS.
Aksyonov K.A., Spitsina I.A., Bykov E.A., Goncharova N.V. Enterprise information systems engineering method based on semantic models of multi-agent resources conversion processes and software // International conference Intelligent Systems and Agents 2008 part of the IADIS Multi Conference on Computer Science and Information Systems 2008 (MCCSIS’08), Amsterdam, The Netherlands, July 22-24, 2008. Proceedings of the IADIS International conference Intelligent Systems and Agents 2008, July 2008. Amsterdam: IADIS, 2008. p.225 – 227.
Ìåòîä ïðîåêòèðîâàíèÿ èíôîðìàöèîííûõ ñèñòåì ïðåäïðèÿòèé, îñíîâàííûé íà ñåìàíòè÷åñêèõ ìîäåëÿõ ìóëüòèàãåíòíîãî ïðîöåññà ïðåîáðàçîâàíèÿ ðåñóðñîâ è ïðîãðàììíîãî îáåñïå÷åíèÿ.
Aksyonov K.A., Spitsina I.A., Bykov E.A., Smoliy E.F. Computer Aided Enterprise Information Systems Engineering with BPsim Studio // Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2008). Singapore: 2008. p.3497–3501.
Àâòîìàòèçèðîâàííîå ïðîåêòèðîâàíèå èíôîðìàöèîííûõ ñèñòåì ïðåäïðèÿòèé â BPsim.
Aktipis A. (2008). A SIMPLE model for the evolution of movement and cooperation: Social dilemmas emerge from interactions with a shared environment // Paper presented at Swarmfest, 2008.
Skoogh Anders, Johansson Björn A methodology for input data management in discrete event simulation projects // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1727-1735.
Discrete event simulation (DES) projects rely heavily on high input data quality. Therefore, the input data management process is very important and, thus, consumes an extensive amount of time. To secure quality and increase rapidity in DES projects, there are well structured methodologies to follow, but a detailed guideline for how to perform the crucial process of handling input data, is missing. This paper presents such a structured methodology, including description of 13 activities and their internal connections. Having this kind of methodology available, our hypothesis is that the structured way to work increases rapidity for input data management and, consequently, also for entire DES projects. The improvement is expected to be larger in companies with low or medium experience in DES.
Anderson Nicholas P., Evans Gerald W. Determination of operating policies for a barge transportation system through simulation and optimization modeling // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.2585-2589.
This paper presents a simulation model of a barge transportation system for petroleum delivery within an inland waterway. The simulation is employed as an evaluation model within a decision support system which also includes a criterion model, represented as a decision maker’s utility function, and an optimization procedure which employs scatter search. Variance reduction techniques are also employed in order to improve the accuracy of the estimates of the performance measures associated with the system. The main purpose of the system is to determine values for important inventory policy variables.
Andersson, M., Ng H. C.A., Grimm, H. Simulation optimization for industrial scheduling using hybrid genetic representation // In Proceedings of the 2008 Winter Simulation Conference, IEEE, Inc., Miami, FL, USA, pp. 2004–2011.
Anderson, P.N., Evans, W.G. Determination of operating policies for a barge transportation system through simulation and optimization modeling // In Proceedings of the 2008 Winter Simulation Conference, IEEE, Inc., Miami, FL, USA, pp. 2585–2589.
Antonova G.M., Tsvirkun A.D. Modern Ability of Optimization-Simulation Approach // Conference Proceedings. 17th World Congress of International Federation of Automatic Control. IFAC’08 (July 6-11, 2008), Seoul, Korea, 2008.
Arunachalam S., Zalila-Wenkstern R., Steiner R. (2008). Environment Mediated Multi Agent Simulation Tools - A Comparison // In Proceedings of the 2008 Second IEEE international Conference on Self-Adaptive and Self-Organizing Systems Workshops (57-62). Washington, DC: IEEE Computer Society.
Osman Balci A review of «Diagnosis of Discrete Event Systems Using Decentralized Architectures» // ACM Computing Reviews, Mar. 6. 2008.
Osman Balci, James D. Arthur, and Richard E. Nance Accomplishing Reuse with a Simulation Conceptual Model // In Proceedings of the 2008 Winter Simulation Conference (Miami, FL, Dec. 7-10). IEEE, Piscataway, NJ, 2008. pp. 959-965.
Banu Yetkin Ekren, Sunderesh S. Heragu Simulation based optimization of multi-location transshipment problem with capacitated transportation // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.2632-2638.
In this study, a single-item two-echelon inventory system where the items can be stored in each of N stocking locations is optimized using simulation. The aim of this study is to minimize the total inventory, backorder, and transshipments costs, based on the replenishment and transshipment quantities. To find out the optimum levels of the transshipment quantities among stocking locations and the replenishment quantities, the simulation model of the problem is developed using ARENA 10.0 and then optimized using the OptQuest tool in this software.
Blikstein P., Abrahamson D., Wilensky U. (2008). The classroom as a complex adaptive system: An agent-based framework to investigate students' emergent collective behaviors // In G. Kanselaar, J. van Merriënboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 312-313). Utrecht, The Netherlands: ISLS.
This study applies agent-based modeling methodology to investigate individual and social factors underlying inequitable participation patterns observed in a real classroom in which an experimental collaborative activity was implemented. We created agent-based simulations of simplified collaborative activities and qualitatively compared results from running the model with the classroom data.
Blikstein P., Wilensky U. (2008). Implementing multi-agent modeling in the classroom: Lessons from empirical studies in undergraduate engineering education // In G. Kanselaar, J. van Merriënboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 266-273). Utrecht, The Netherlands: ISLS.
Briggs David Embedding human scheduling in a steel plant simulation // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1959-1967.
A simulation was commissioned to understand the interactions that constrain the capacity of a steel plant. The aim was for this to become a reusable tool that could evaluate the effect of future changes to market requirements and operational practices. This paper describes how a simulation model incorporating human decisionmaking was conceived and constructed. The use of simulation as a tool for knowledge capture in scheduling is considered. The resulting tool has been in use for four years and has acted as a driver to reconsider where the real processing bottlenecks are and what part scheduling can play in managing them.
Burnett Gabriel A., Finke Daniel A., D. J. Medeiros, Traband Mark T. Automating the development of shipyard manufacturing models // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1761-1767.
Simulation results are often needed within a short time frame, while the development of simulation models can be time consuming. We develop a methodology to facilitate rapid generation of simulation models from an enterprise database. Data is communicated between PLM software and Flexsim using a standard Microsoft Excel format. We have developed a custom Flexsim interface and software-specific model generator that creates a discrete event simulation model from the PLM input data. Preliminary results show that the methodology can reduce the cost of simulation model generation while simultaneously improving the accuracy of generated models. This work highlights the benefits of automatic model generation techniques, describes a shipbuilding implementation of the methodology, and provides direction for future work.
Caballini C. A system dynamics model for the simulation of a non multi echelon supply chain: analysis and optimization utilizing the Berkeley Madonna software / C. Caballini, R. Revetria // Int. journal of mathematical models and methods in applied sciences. — 2008. — Vol. 2, Is. 4. — pp. 503-512.
Caricato Pierpaolo, Grieco Antonio, Nucci Francesco Simulation and mathematical programming for a multi-objective configuration problem in a hybrid flow shop // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1820-1828.
This paper introduces an application of simulation-based multi-objective optimization to solve a system configuration problem in a hybrid flow shop system. The test case is provided by a firm that manufactures mechanical parts for the automotive sector. We present an architecture that uses both discrete-event simulation and mathematical programming tools in order to solve the problem. The multiple objective nature of the problem is preserved throughout the proposed approach, using Pareto-dominance concepts both to eliminate inefficient solutions within the proposed solution algorithm and to provide the user with efficient solutions. Mathematical programming is used to cull the required number of simulation runs. Computational results obtained using a real-world case study are reported. The proposed approach is benchmarked against a general purpose simulation-optimization engine in order to prove its effectiveness.
Chu C.J. Maestre F.T., Xiao S., Weiner J., Wang Y.S., Duan Z.H., Wang G. 2008. Balance between facilitation and resource competition determines biomass-density relationships in plant populations // Ecology Letters 11: 1189-1197.
Crooks A., Castle C., Batty M. (2008). Key challenges in agent-based modelling for geo-spatial simulation // Computers, Environment and Urban Systems 32(6), pp.417-430.
Damiron C., Nastasi A. Discrete Rate Simulation Using Linear Programming // Proceedings of the 2008 Winter Simulation, IEEE, Inc., Piscataway, NY. — 2008. — P. 740–749.
Tobias Deutsch, Tehseen Zia, Roland Lang, Heimo Zeilinger A Simulation Platform for Cognitive Agents // 2008.
To be able to develop reasoning units based on findings from sciences like behaviorism, psychology, neurology, and psychoanalysis, a test bed which is close to these sciences is needed. A possible approach is to use cognitive agents situated within a game of artificial life. Using such a simulated environment reduces the abstraction step from the origin science to the technical system. This article gives a short overview on such a game of life and discusses several design issues. The simulator is implemented using the simulation software AnyLogic.
Diappi L., Bolchi P. (2008). Smith's rent gap theory and local real estate dynamics: A multi-agent model. Computers, Environment and Urban Systems 32(1), pp.6-18.
Duggan J. Equation-based policy optimization for agent-oriented system dynamics models // System Dynamics Review Vol. 24, No. 1. – Spring 2008. – Ð.97-118.
Dukulis I. Optimization Models for Biofuel Logistic Systems / I. Dukulis, G. Birzietis, D. Kanaska // Proceedings of the 7th International Scientific Conference «Engineering for Rural Development». – Jelgava: LUA. – 2008. – P. 283-289.
Dukulis Ilmars Using of Anylogic and extendsim in modelling of biofuel logistic systems // 2008 ã.
Ilmars Dukulis, Gints Birzietis, Daina Kanaska Optimization models for biofuel logistic systems // Engineering for rural development. Latvia. Jelgava, 29.-30.05.2008. P.283-289.
The short overview of the strategies and action plans in production and use of biofuels in Europe and Latvia is given. The characteristic of biofuel supply chain as a system is explained. Existing solutions in improvement of biofuel logistic systems are analysed as well as available tools for the modelling of biofuel supply chains. As the first system for modelling the pure vegetable oil from rapeseed was chosen and first results of modelling are discussed.
Eamonn L. Introduction to Agent-Based Simulation in Flexsim, Flexsim Corporation. – Oct. 2008.
Earnest D.C. (2008). Voting Complexity and Electoral Outcomes: An Agent-Based Model of Condorcet Social Choice Problems // Complexity and Policy Analysis: Decision Making in an Interconnected World. Kurt A. Richardson, Linda Dennard and Goktug Morcol, eds. Greenwich, CT: Information Age Publishing: 2008. pp.147-166.
Earnest D.C. (2008). Coordination in Large Numbers: An Agent-Based Model of International Negotiations // International Studies Quarterly 52, 2: 363-382.
Eckert C., Fliege F., Steinhauer D. Simulation of logistics processes in RoRo terminals based on building blocks, 13th ASIM Conf. on Simulation in Production and Logistics, Berlin, 2008. pp.101- 110.
Ekren, Y.B., Heragu, S.S. Simulation based optimization of multi-location transshipment problem with capacitated transportation // In Proceedings of the 2008 Winter Simulation Conference, IEEE, Inc., Miami, FL, USA, pp. 2632–2638.
Fahimnia B., Luong L., Marian R. An integrated model for the optimization of a two-echelon supply network // Journal of Achievements in Materials and Manufacturing Engineering. 2008. Vol. 31, Issue 2. P. 477–484.
Faulin J., Gilibert M., Juan A., Ruiz R., Vilajosana X. (2008). SR-1: A simulation-based algorithm for the capacitated vehicle routing problem. In: Proceedings of the Winter Simulation Conference, pp. 2708-2716.
Feldman T., Friedmam D., Abraham R. (2008). Bubbles & Crashes: An Experimental Approach // Retrieved February 25, 2010.
Francisco M. (2008). Designing classrooms with ABM // Paper presented at Swarmfest, 2008.
Fritzsche Mathias, Johannes Jendrik, Zschaler Steffen, Zherebtsov Anatoly, and Terekhov Alexander. Application of Tracing Techniques in Model-Driven Performance Engineering // 2008 ãîä.
This paper discusses various existing traceability methodologies and describes their application and extension for Model-Driven Performance Engineering by taking its specific needs into account.
Fu Michael C., Chun-Hung Chen, Shi Leyuan Some topics for simulation optimization // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.27-38.
We give a tutorial introduction to simulation optimization. We begin by classifying the problem setting according to the decision variables and constraints, putting the setting in the simulation context, and then summarize the main approaches to simulation optimization. We then discuss three topics in more depth: optimal computing budget allocation, stochastic gradient estimation, and the nested partitions method. We conclude by briefly discussing some related research and currently available simulation optimization software.
Gary Michael Shayne, Kunc Martin, Morecroft John D.W. and Rockart Scott F. System dynamics and strategy // System Dynamics Review Volume 24 Number 4 Winter 2008. P.407-428.
Gilbert G. (2008) Agent-based models // Sage Publications, Inc.
Gorodetsky V.I., Karsaev O.V., Konyushy O.V., Samoylov V.V. MAS-based simulation modelling for flight traffic management // MSTU CA Scientific Journal, series «Navigation and Flight Traffic Management». 2008.
Ingalls Ricki G. Introduction to simulation // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.17-26.
Simulation is a powerful tool if understood and used properly. This introduction to simulation tutorial is designed to teach the basics of simulation, including structure, function, data generated, and its proper use. The introduction starts with a definition of simulation, goes through a talk about what makes up a simulation, how the simulation actually works, and how to handle data generated by the simulation. Throughout the paper, there is discussion on issues concerning the use of simulation in industry.
Janota A. (2008). MAS Model of the Level Crossing // International Journal of ITS Research, Japan, Vol. 6, No. 2 (p. 111-116). ISSN 1348-8503.
Janssen M.A., Bushman C. (2008). Evolution of cooperation and altruistic punishment when retaliation is possible // Journal of Theoretical Biology 254: 451-455.
Jolly R., Wakeland W.W. (2008). Using Agent Based Simulation and Game Theory Analysis to Study Information Sharing in Organizations - The InfoScape // HICSS 2008: 335.
Jones G.T. (2008). Dynamical Jurisprudence: Law as a Complex System // Georgia State University Law Review, 24(4), pp.873-883.
Jovani R., Grimm V. 2008. Breeding synchrony in colonial birds: from local stress to global harmony // Proceedings of the Royal Society of London B 275:1557-1563.
Jurenoks V., Jansons V., Didenko K. Modelling of Stability of Economic Systems Using Benchmarking and Dynamic Programming // X International Conference on Computer Modelling and Simulation EUROSIM/UKSim, April 1–3, 2008, Cambridge, United Kingdom, 2008, p.311-316.
Kanarek A., Lamberson R., Black J.M. (2008). An individual-based model for traditional foraging behavior: investigating effects of environmental fluctuation // Natural Resource Modeling, Vol 21 (1), pp. 93-116.
Kirchhof Patrick, Meseth Nicolas, Witte Thomas Simulation based evaluation of the workload control concept for a company of the automobile industry // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1856-1862.
This paper describes a simulation study conducted for a company of the German automobile supply industry facing the need to improve delivery reliability. The intention of the study was to evaluate whether Workload Control (WLC) is applicable as production control policy for this company and whether improvement can be expected.
Kleijnen J. (2008). Design of experiments: overview. In: Proceedings of the Winter Simulation Conference, pp. 479-488.
Krahl David ExtendSim 7 // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.215-221.
ExtendSim 7 is a proven simulation environment capable of modeling a wide range of systems. ExtendSim 7 is used to model continuous, discrete event, discrete rate, and agent based systems. ExtendSim’s design facilitates every phase of the simulation project, from creating, validating, and verifying the model, to the construction of a user interface that allows others to analyze the system. Simulation tool developers can use ExtendSim’s built-in, compiled language, ModL, to create reusable modeling components. All of this is done within a single, self-contained software program that does not require external interfaces, compilers, or code generators. This paper will introduce ExtendSim 7, demonstrate its advanced technology and features, and explain sample simulation applications.
Kuhl Michael E., Lada Emily K., Steiger Natalie M., Wagner Mary Ann, Wilson James R. Introduction to modeling and generating probabilistic input processes for simulation // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.48-61.
Techniques are presented for modeling and generating the univariate probabilistic input processes that drive many simulation experiments. Emphasis is on the generalized beta distribution family, the Johnson translation system of distributions, and the Bézier distribution family. Also discussed are nonparametric techniques for modeling and simulating time-dependent arrival streams using nonhomogeneous Poisson processes. Public-domain software implementations and current applications are presented for each input-modeling technique. Many of the references include live hyperlinks providing online access to the referenced material.
Law Averill M. How to build valid and credible simulation models // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.39-47.
In this tutorial we present techniques for building valid and credible simulation models. Ideas to be discussed include the importance of a definitive problem formulation, discussions with subject-matter experts, interacting with the decision-maker on a regular basis, development of a written assumptions document, structured walk-through of the assumptions document, use of sensitivity analysis to determine important model factors, and comparison of model and system output data for an existing system (if any). Each idea will be illustrated by one or more realworld examples. We will also discuss the difficulty in using formal statistical techniques (e.g., confidence intervals) to validate simulation models.
Lawson Barry, Leemis Lawrence Monte Carlo and discrete-event simulations in C and R // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.11-16.
The Monte Carlo and discrete-event simulation code associated with the Simulation 101 pre-conference workshop (offered at the 2006, 2007, and 2008 WSC) is available in both C and R. This paper begins with general instructions for downloading, compiling, and executing the software. This is followed by detailed explanations of two programs that are representative of the software suite: craps uses Monte Carlo simulation to estimate the probability of winning the dice game Craps, and ssq2 uses discrete-event simulation to estimate several measures of performance associated with a single-server queue.
Le Q.B., Park S.J., Vlek P.L., Cremers A.B. (2008). Land-Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human-landscape system. I. Structure and theoretical specification // Ecological Informatics 3(2), pp.135-153.
Leitner Daniel, Kropf Johannes, Zauner Gunther, Karpov Yuri, Senichenkov Yuri, Kolesov Yuri. Modeling of Structural-dynamic Systems by UML Statecharts in AnyLogic // Journal «Simulation News Europe», Volume 18, Number 2, August 2008, ISSN 0929-2268. P.12-16.
With the progress in modeling dynamic systems new extensions in model coupling are needed. Depending on the general conditions of the system the description of the model and thereby the state space is altered. This change of system behavior can be implemented in different ways. In this work we focus on AnyLogic and its ability to switch between different sets of equations using UML statecharts. Different possibilities of the coupling of the state spaces are compared. This can be done either using a parallel model setup, a serial model setup, or a combined model setup. The analogies and discrepancies can be figured out on the basis of three classical examples.
Levy S.T., Wilensky U. (2008). Inventing a «mid-level» to make ends meet: Reasoning through the levels of complexity // Cognition and Instruction, 26(1), 1-47.
E. Lopez-Neri, E. Lopez-Mellado, and A. Ramirez-Treviño, Microscopic Modeling Framework for Urban Traffic Systems Simulation // In the 7th International conference on system simulation and scientific computing, Beijing, China, 2008.
Macal Charles M., North Michael J. Agent-based modeling and simulation: abms examples // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.101-112.
Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, the threat of bio-warfare, and the factors responsible for the fall of ancient civilizations. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing agent models, and illustrates the development of a simple agent-based model.
Martin-Villalba C., Urquia A. and Dormido S. An approach to virtual-lab implementation using Modelica // Mathematical and Computer Modelling of Dynamical Systems, 14(4), 2008, pp. 341–360.
McLean Aldo A., Biles William E. A simulation approach to the evaluation of operational costs and performance in liner shipping operations // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.2577-2584.
This paper presents a simulation model of the operation of a liner shipping network that considers multiple service routes and schedules.
Medeiros D., Swenson E., DeFlitch C. (2008). Improving patient flow in a hospital emergency department. In: Proceedings of the Winter Simulation Conference, pp. 1526-1531.
Menezes R., Bullen H. (2008). A study of terrain coverage models // Proceedings of the 2008 ACM symposium on Applied computing.
Menke N. (2008). Modeling the Effects of Trauma on Epidermal Wound Healing // Paper presented at Swarmfest, 2008.
Minghui Yang Using data driven simulation to build inventory model // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.2595-2599.
Many general-purpose simulation languages (such as Arena, SLAM II, GPSS/H, Siman etc) have been the major simulation tools to simulate the demand-supply processes. They have made great contributions to the decision making. But in the recent years, followed by the fast development of Window applications, powerful PC hardware and software, numerous applications have used different approaches to develop simulation applications. One rapidly developing area in simulation is dynamic data-driven simulation by using data manipulation and analysis packages. SAS is a powerful tool for data analysis and data manipulation. It can also be used to build simulation models in data driven applications. This article presents our research development in this area for demand forecast applications.
Minh Dang Nguyen, Soemon Takakuwa Emergence of simulations for manufacturing line designs in Japanese automobile manufacturing plants // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1847-1855.
The aim of this research is to introduce the reader to a new perspective on the framework for designing a manufacturing line project in Japanese automobile manufacturingplants.
Gasper Music, Borut Zupancic. Discrete Hybrid Automata Approach to Structural Dynamic Modelling and Simulation // Journal «Simulation News Europe», Volume 18, Number 2, August 2008, ISSN 0929-2268. P.5-11.
The paper presents the discrete hybrid automata (DHA) modelling formalism and related HYSDEL modelling language. The applicability of the framework in the context of modelling of structural-dynamic systems is discussed. High level and partially modular modelling capabilities of HYSDEL are presented and the possibility of modelling structural-dynamic systems is shown and illustrated by a simple example. To model structural dynamics, standard HYSDEL list structures are employed, and additional dynamic modes are introduced when state re-initializations are necessary at mode switching. For the derived DHA models an efficient simulation algorithm is presented. The main features of the framework are compared to characteristics of other modelling and simulation tools capable of capturing structural dynamics.
Najafi Mosoud. Selection of Variables in Initialization of Modelica Models // Journal «Simulation News Europe», Volume 18, Number 2, August 2008, ISSN 0929-2268. P.33-41.
In Scicos, a graphical user interface (GUI) has been developed for the initialization of Modelica models. The GUI allows the user to fix/relax variables and parameters of the model as well as change their initial/guess values. The output of the initialization GUI is a pure algebraic system of equations which is solved by a numerical solver. Once the algebraic equations solved, the initial values of the variables are used for the simulation of the Modelica model. In this paper, we present the way the incidence matrix associated with the equations of the system can be exploited to help the user to select variables to be fixed and to set guess values of the variables during the initialization phase.
Nakayama Marvin K. Statistical analysis of simulation output // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.62-72.
We discuss methods for statistically analyzing the output from stochastic discrete-event or Monte Carlo simulations. Terminating and steady-state simulations are considered.
Niazi M. (2008). Self-organized customized content delivery architecture for ambient assisted environments // In Proceedings of the Third international Workshop on Use of P2p, Grid and Agents For the Development of Content Networks (Boston, MA, USA, June 23-23, 2008). HPDC, UPGRADE '08. ACM, New York, NY, pp.45-54.
Niazi M., Baig A.R. (2008). Growth of Research Institutes in Developing Nations // International Research Conference 2008, West Visayas State University, La Paz Iloilo City, Philippines, Feb 27- 29, 2008.
Niazi M., Hussain A., Baig A.R., Bhatti S. (2008). Simulation of the research process // in Winter Simulation Conference, Miami, FL, pp.1326-1334.
Victor Okol'nishnikov, Alexander Zenzin. Use of Simulation for Development of Process Control System. // Proc. of IEEE region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering (SIBIRCON 2008) — Novosibirsk, 2008. — P. 248 – 251.
Okuyama T. (2008). Intraguild predation with spatially structured interactions // Basic and Applied Ecology 9(2), pp.135-144.
Pathak S.A., Jacobson M.J., Kim B., Zhang B., Feng D. (2008). Learning the Physics of Electricity with Agent-Based Models: The paradox of productive failure // Paper presented at the 2008 International Conference on Computers in Education.
Pegden C. Dennis Introduction to SIMIO // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.229-235.
This paper describes a new modeling system – Simio that is designed to simplify model building by promoting a modeling paradigm shift from the process orientation to an object orientation. Simio is a simulation modeling framework based on intelligent objects. The intelligent objects are built by modelers and then may be reused in multiple modeling projects. Although the Simio framework is focused on object-based modeling, it also supports a seamless use of multiple modeling paradigms including event, process, object, and agent-based modeling.
Pegden, D. SIMIO: A new simulation system based on intelligent objects // In Proceedings of the 2008 Winter Simulation Conference, eds. S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler, 2293-2300. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
B.N. Pishchik, L.A. Vorontsova, P.V. Iosifov, V.D. Neskorodev, V.V. Okol'nishnikov, T.M. Osokina, A.I. Fedorov, and D.V. Chernakov. Development of an Automatic Process Control System for the Severomuiskii Tunnel // Optoelectronics, Instrumentation and Data Processing, 2008, Vol. 44, No. 3, pp. 279–284.
Rabe Markus, Spieckermann Sven, Wenzel Sigrid A new procedure model for verification and validation in production and logistics simulation // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1717-1726.
Verification & Validation of simulation models and results has been strongly investigated in the context of defence applications. Significantly less substantial work can befound for applications for production and logistics, which is surprising when taking into account the massive impact that wrong or inadequate simulation results can have on strategic and investment-related decisions for large production and logistics systems. The authors have, therefore, founded an expert group for this specific topic in the year 2003, which has analysed the existing material and then developed proposals for definitions, overviews on existing V&V techniques, practical hints for the documentation of the procedural steps within a simulation study, and a specific procedure model for V&V in the context of simulation for production and logistics. The results of this working group are available as a textbook, in German. This paper summarises major results.
Rand W., Blikstein P., Wilensky U. (2008). GoGoBot: Group collaboration, multi-agent modeling and robots // In L. Padgham, D. Parkes, J. Müller & S. Parsons (Eds.), Proceedings of the 7th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS (Vol. 3, pp. 1717-1722). Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).
Raychaudhuri S. (2008). Introduction to Monte Carlo Simulation. In: Proceedings of the Winter Simulation Conference, pp. 91-100.
Roongrat Chatabush, Rosenberger Jay, Huff Brian Simulation of unit loading device inventory in airline operations abstract // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.2668-2672.
Commercial airlines often encounter imbalances in their inventory of unit loading devices (ULDs). A stochastic simulation model was developed to evaluate inventory policies. The structure of the simulation model is described. We evaluate a minimum ULD loading configuration policy and demonstrate how it reduces ULD shortages and helps balance ULD network flow and inventory. As a result, airlines can reduce operating expenses and improve customer service. Finally, we give future directions for studying ULD inventory.
Rosenblatt Olaf Enge, Bastian Jens, Claub Christoph, Schwarz Peter. Numerical Simulation of Continuous Systems with Structural Dynamics // Journal «Simulation News Europe», Volume 18, Number 2, August 2008, ISSN 0929-2268. P.24-32.
In this paper, «continuous systems with structural dynamics» shall be understood as dynamical systems consisting of components with continuous and/or discrete behaviour. Continuous systems with structural dynamics - or so-called «hybrid systems» - can often be investigated only by a so-called «hybrid simulation» which means a simultaneous simulation of continuous-time dynamics (modelled by differential equations or differential-algebraic equations (DAE)) and discrete-event dynamics. To this end, an algorithm for numerical simulation of hybrid systems must be able to both solve a DAE system within a «continuous» time progression as well as to deal with event-driven phenomena.
Russell E., Wilensky U. (2008, May). Consuming spatial data in NetLogo using the GIS Extension // Paper presented at the annual meeting of the Swarm Development Group, Chicago, IL.
Sakellariou I., Kefalas P., Stamatopoulou I. (2008). Enhancing NetLogo to Simulate BDI Communicating Agents // In J. Darzentas et al. (Eds.), Proceedings of 5th Hellenic Conference on Artificial Intelligence, SETN 08, (pp. 263-275). Syros, Greece: Springer-Verlag.
Sakellariou I., Kefalas P., Stamatopoulou I. (2008). Teaching Intelligent Agents using NetLogo // Paper presented at the ACM-IFIP Informatics Education Europe III Conference, IEEIII 2008, Venice, Italy, December 4-5, 2008.
Sang C. Park, Chang Mok Park, Gi-Nam Wang, Jongeun Kwak, Sungjoo Yeo PLCStudio: simulation based PLC code verification // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.222-228.
Proposed in this paper is the architecture of a PLC programming environment that enables a visual verification of PLC programs. The proposed architecture integrates a PLC program with a corresponding plant model, so that users can intuitively verify the PLC program in a 3D graphic environment. The plant model includes all manufacturing devices of a production system as well as corresponding device programs to perform their tasks in the production system, and a PLC program contains the control logic for the plant model. For the implementation of the proposed PLC programming environment, it is essential to develop an efficient methodology to construct a virtual device model as well as a virtual plant model. The proposed PLC programming environment provides an efficient construction method for a plant model based on the Discrete Event Systems Specifications formalism, which supports the specification of discrete event models in a hierarchical, modular manner.
Sanjay Jain Tradeoffs in building a generic supply chain simulation capability // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1873-1881.
Building a simulation model for any large complex system requires high expertise and effort. These requirements can be reduced through building generic simulation capability that includes artifacts for facilitating the development of the simulation model. The artifacts can have a range of capabilities depending on the design goals for the simulation. This paper focuses on issues to be considered in building a generic simulation capability for supply chains. A number of approaches used in recent years for building generic supply chain simulation capability are discussed. Such approaches include data-driven simulators, interactive simulators, and sub-models for supply chain components. Tradeoffs are identified that should be considered in selecting an approach for building a generic supply chain simulation capability.
Sanz Victorino, Urquia Alfonso, Dormido Sebastian. Introducing Messages in Modelica for Facilitating Discrete-Event System Modeling // Journal «Simulation News Europe», Volume 18, Number 2, August 2008, ISSN 0929-2268. P.42-53.
The work performed by the authors to provide to Modelica more discrete-event system modeling functionalities is presented. These functionalities include the replication of the modeling capacities found in the Arena environment, the SIMAN language and the DEVS formalism. The implementation of these new functionalities is included in three free Modelica libraries called ARENALib, SIMANLib and DEVSLib. These libraries also include capacities for random number and variates generation, and dynamic memory management. As observed in the work performed, discrete-event system modeling with Modelica using the process-oriented approach is difficult and complex. The convenience to include a new concept in the Modelica language has been observed and is discussed in this contribution. This new concept corresponds to the model communication mechanism using messages.
Sargent R.G. 2008. Verification and validation of simulation models // In Proceedings of the 2008 Winter Simulation Confe-rence. Miami, FL, 157-169.
Sasso Daniel, Biles William E. An object-oriented programming approach for a GIS data-driven simulation model of traffic on an inland waterway // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.2590-2594.
This research proposes the integration of a Geographic Information System (GIS) with the Arena Simulation software to model the transit of ocean-going vessels through the Panama Canal. The purpose of this integration is to initialize the simulation model with the vessels that are currently transiting the system and the ones ready to begin their transit taking into account waiting time in queue, booking status, navigation restrictions and their times through the locks.
Savrasov, M., Toluyew, Y. Transport system’s mesoscopic model validation using simulation on microlevel // In: Proceeding of 8th International Conference, Reliability and Statistics in Transportation and Communication, 2008. pp. 297-304.
The paper presents the conceptual mesoscopic model of the defined transport network and its implementation with MS Excel plus VBA. The modelling task is to estimate the dynamics of all the queues and the crossroads capacity utilization. The mesoscopic approach is quite new; there is only one paper, which validates mesoscopic approach, done for queuing systems. That is why the task of mesoscopic models validation is implemented. To validate mesoscopic model we are using simulation on microscopic level, by development of the model in simulation package PTV VISION VISSIM 5.1, which is widely used for traffic simulation.
Schriber Thomas J., Brunner Daniel T. Inside discrete-event simulation software:
how it works and why it matters // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.182-192.
This paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation software works. Topics include discrete-event systems; entities, resources, control elements and operations; simulation runs; entity states; entity lists; and entity-list management. The implementation of these generic ideas in AutoMod, SLX, and Extend is described. The paper concludes with several examples of “why it matters” for modelers to know how their simulation software works, including coverage not only of AutoMod, SLX, and Extend, but also of SIMAN (Arena), ProModel, and GPSS/H.
Schumann D., Simon A. (2008). Public acceptance of CO2 capture and storage (CCS): Simulating the impact of communication // Paper presented at ZUMA (Zentrum für Umfragen, Methoden und Analysen, Centre for surveys, methods and analyses) Advanced Simulation Workshop, April 8-11, Koblenz.
Sengupta P., Wilensky U. (2008, March). Designing across ages: On the low-threshold-high-ceiling nature of NetLogo-based learning environments // Paper presented at the annual meeting of the American Educational Research Association, New York, NY.
Sengupta P., Wilensky U. (2008). Learning activities as tools for formative assessment - Case study of a computational multi-agent based electricity curriculum (NIELS: NetLogo Investigations In Electromagnetism) // In G. Kanselaar, J. van Merrieboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 383-391). Utrecht, The Netherlands: ISLS.
Sengupta P., Wilensky U. (2008). On learning electricity in 7th grade with multi-agent based computational models (NIELS) // In G. Kanselaar, J. van Merriëboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 123-125). Utrecht, The Netherlands: ISLS.
Sengupta P., Wilensky U. (2008). On the learnability of electricity as a complex system // In G. Kanselaar, J. van Merriëboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 258-264). Utrecht, The Netherlands: ISLS.
Sengupta P., Wilensky U. (2008, March). On the representational and epistemological affordances of NetLogo-based science curricula // Paper presented at the annual meeting of the American Educational Research Association, New York, NY.
Sharda Bikram, Bury Scott J. A discrete event simulation model for reliability modeling of a chemical plant // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1736-1740.
This paper discusses a discrete event simulation model developed to identify and understand the impact of different failures on the overall production capabilities in a chemical plant. The model will be used to understand key equipment components that contribute towards maximum production loss and to analyze the impact of a change policy on production losses. A change policy can be classified in terms of new equipment installation or increasing the stock level for the failure prone components. In this paper, we present the approach used and some preliminary results obtained from available data.
Shresta Sanjiv, Mayer Ralf H. Modeling of air traffic arrival operations through agent-based simulation // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.2673-2681.
This paper reports on the development and validation of an agent based simulation model of air traffic control arrival operations. The simulation model includes modeling of both the structure and procedures of air traffic operations. It is thus suitable for evaluating the impacts of shifts in those structures and procedures. Three key operational metrics are introduced which are sensitive to the internal workings of air traffic arrival operations. The simulation model is validated by demonstrating agreement in those key metrics between the simulation and a set of baseline arrival operations radar data. After the simulation model has been shown to reproduce actual operations, select details of the simulation can be altered to incorporate proposed operational changes. The impact of the changes on the computer simulation will offer a prediction of howthe operational changes will affect actual operations.
Siamak Tavakoli, Alireza Mousavi, Alexander Komashie A generic framework for real-time discrete event simulation modelling // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1931-1938.
This paper suggests a generic simulation platform that can be used for real-time discrete event simulation modeling. The architecture of the proposed system is based on a tested flexible input data architecture developed in Labview, a real-time inter-process communication module between the Labview application and a discrete event simulation software (in this case Arena). Two example applications in the healthcare and manufacturing sectors are provided to demonstrate the ease of adaptability to such physical system.
Smith Jeffery S., Cho Younchol Offline commissioning of a PLC-based control system using ARENA // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.1806-1810.
In this paper, we address a generalized method of mapping a control system simulation model to the PLC emulator being tested using model variables and PLC tags under the offline commissioning environment. For this research we created an example system similar to a high speed packaging system described in a previous WSC paper. Implementation experience using Rockwell Software applications is provided.
Sokolov B.V., Fridman A. Integrated Situational Modelling of Industry-Business Processes for Every Stage of Their Life Cycle // 4th International IEEE Conference on Intelligent Systems (IS’2008), Varna, Bulgaria, September 6–8, 2008. Proceedings, Volume I.
Sokolov B.V., Ivanov D.A., Verzilin D.N., Zaychik E.M. Models and an Algorithm for Multi-Criteria Synthesis of Control // 22nd European Conference on Modelling and Simula-tion (ECMS 2008), Nicosia, Cyprus, June, 3–6, 2008.
Sokolov B.V., Ohtiliev Ì., Verzilin D.N., Zaychik E.M., Èêîííèêîâà À.V. Intellectual Information Technologies of Integrated Modeling Structure-Functional Synthesis of Reconfigur-able Logistics Systems // Ðîññèéñêî-Ãåðìàíñêàÿ êîíôåðåíöèÿ ïî ëîãèñòèêå (DR-LOG'08), Ðîññèÿ, Ìîñêâà, 21-25 ìàÿ, 2008 ã. Ñáîðíèê ñòàòåé.
Soyka M., Steinhauer D. Simulation of logistic processes on board of vessels // 13th ASIM Conf. on Simulation in Production and Logistics, Berlin, 2008. pp. 91-100.
Stonedahl F., Kornhauser D., Russell E., Brozefsky C., Verreau E., Tisue S., Wilensky U. (2008, May). Tinkering with turtles: An overview of NetLogo's Extensions API // Paper presented at the annual meeting of the Swarm Development Group, Chicago, IL.
Stonedahl F., Rand W., Wilensky U. (2008, July). CrossNet: A framework for crossover with network-based chromosomal representations // Paper presented at the 2008 Genetic and Evolutionary Computation Conference (GECCO), Atlanta, GA.
Stonedahl F., Rand W., Wilensky U. (2008, May). Multi-agent learning with a distributed genetic algorithm: Exploring innovation diffusion on networks // Paper presented at the Seventh International Conference on Autonomous Agents and Multi-agent Systems (AAMAS), Estoril, Portugal.
Lightweight agents distributed in space have the potential to solve many complex problems. In this paper, we examine a model where agents represent individuals in a genetic algorithm (GA) solving a shared problem. We examine two questions: (1) How does the network density of connections between agents affect the performance of the systems? (2) How does the interaction topology affect the performance of the system?
Sturrock David T. Tips for successful practice of simulation // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.85-90.
Succeeding with a technology as powerful as simulation involves much more than the technical aspects you may have been trained in. The parts of a simulation study that are outside the realm of modeling and analysis can make or break the project. This paper explores the most common pitfalls in performing simulation studies and identifies approaches for avoiding these problems.
Sudhira H.S. (2008). Modelling the Strategic Interactions in Urban Governance // In Proceedings of Third International Conference on Public Policy and Governance, Centre for Public Policy, Indian Institute of Management - Bangalore, India.
Syberfeldt Anna, Grimm Henrik, Ng Amos, Andersson Martin, Karlsson Ingemar Simulation-based optimization of a complex mail transportation network // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.2625-2631.
This paper describes the optimization of transport solutions using evolutionary algorithms coupled with the simulation model. The vast transportation network in combination with a large number of possible transportation configuration sand conflicting optimization criteria make the optimization problem very challenging. A large number of simulation evaluations are needed before an acceptable solution is found, making the computational cost of the problem severe. To address this problem, a computationally cheap surrogate model is used to offload the optimization process.
Tolujew, J., Savrasov, M. Mesoscopic approach to modelling a traffic system // In: Proceeding of International Conference Modelling of Business, Industrial and Transport Systems, Transport and Telecommunication Institute, Riga, 2008, pp. 147-151.
Usher C, Tilson L, Olsen J., Jepsen M., Walsh C., Barry M., Jepsen M.R. (2008) Cost-effectiveness of human papillomavirus vaccine in reducing the risk of cervical cancer in Ireland due to HPV types 16 and 18 using a transmission dynamic model // Vaccine , 26(44), pp. 5654-5661.
Vasileva, S. 2PL Algorithm Model of Distributed Transactions with Primary Copies. // International Scientific Conference Informatics in the Scientific Knowledge 2008, Varna Free University «Chernorizets Hrabar» and The Institute of Mathematics and Informatics at the Bulgarian academy of Sciences, June 26-28, 2008, VFU «Chernorizets Hrabar», pp.247-258.
Vasileva, S., P. Milev. Algorithm modeling Distributed Two-Phase Locking of distributed transactions. // International Conference Automatics and Informatics’08, Conference Proceedings, 2008, Sofia, Bulgaria, pp. VIII-41 – VIII-44.
Vasileva, S., A. Milev. Simulation models of two-phase locking of distributed transactions. // Proceedings of the 2008 international conference on Computer systems and technologies, ACM, New York, NY, USA ©2008, Article No. 74, 5 p.
Vasudevan Karthik Krishna, Lote Ravi, Williams Edward J., Ulgen Onur. Iterative use of simulation and scheduling methodologies to improve productivity // Proceedings of the 2008 Winter Simulation Conference S. J. Mason, R. Hill, L. Moench, and O. Rose, eds.
Vaughan T. (2008). In search of the memory less property. In: Proceedings of the Winter Simulation Conference, pp. 2572-2576.
Voinov A.A., Sven Erik J., Brian F. (2008). Software // Encyclopedia of Ecology (pp. 3270-3277). Oxford: Academic Press.
Wang J., Dam G., Yildrim S., Rand W., Wilensky U., Houk J.C. (2008). Reciprocity between the cerebellum and the cerbral cortex: Nonlinear dynamics in microscopic modules // Complexity, 14(2), pp.29-45.
Wei Wang, Young M. Lee, Jin Dong, Feng Cheng, Hongwei Ding, Changrui Ren, Minmin Qiu An introduction to IBM general business simulation environment // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.2700-2707.
IBM General Business Simulation Environment (GBSE) is a supply chain simulation tool developed by IBM China Research Lab. It can capture supply chain dynamics with finest level of granularity and provides great insights to a supply chain’s real operations. GBSE is designed for tactical level decision making; it is proper for supply chain what-if analysis and risk analysis. GBSE implements multiple supply chain processes to considerable details, such as order handling process, inventory control process, manufacturing process, transportation process, procurement process, and planning. The environment is created as a desktop software tool based on Eclipse platform. The backbone framework consists of Presentation Layer, Controller Layer, Service Layer, and Data Layer.
Weisberg M., Reisman K. (2008). The Robust Volterra Principle // Philosophy of Science, 75, pp.106–131.
Weiss, W. 2008. Dynamic Security: An Agent-Based Model for Airport Defense // In Proceedings of the 2008 Winter Simulation Conference, S. J. Mason, R. R. Hill, L. Moench, O. Rose. IEEE, Piscataway NJ.
Whitmeyer J., Carmichael T., Eichelberger C., Hadzikadic M., Khouja M., Saric A., Sun M. (2008). A Computer Simulation Laboratory for Social Theories // Paper presented at the 2008 IEEE/WIC/ACM International Conference on Intelligence Agent Technology, Sydney, Australia, December 2008.
Wilkerson-Jerde M., Sengupta P., Wilensky U. (2008). Perceptual supports for sense-making: A case study using multi-agent based computational learning environments // In G. Kanselaar, J. van Merriënboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the Eighth International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 151-152). Utrecht, The Netherlands: ISLS.
Many studies have examined how learners make sense of the traditionally difficult ideas of levels and emergence in complex systems by interacting with visuospatial multi agent based models. In this poster, we review these findings through the lens of human basic perceptual/representational systems. We argue that many of learners’ observed strategies and explanations surrounding the ideas of levels and emergence are supported by visualizations that leverage perceptual systems related to objects, motion, and geometry.
Zauner Gunther, Judex Florian, Schwarz Peter. Classical and Statechart-based Modeling of State Events and of Structural Changes in the Modelica Simulator Mosilab // Journal «Simulation News Europe», Volume 18, Number 2, August 2008, ISSN 0929-2268. P.17-23.
Mosilab (MOdelling and SImulation LABoratory) a new simulation system developed by Fraunhofer understands Modelica, offers different modeling approaches, and supports structural dynamic systems. This will be discussed on the basis of a main example, the classical constrained pendulum. We show how the solution can be done using only standard Modelica components, where the benefits are and which kind of switching the states can be done. As we will see there is no possibility to define separate submodels with different state space dimensions and switch between these systems during one simulation run.
Zimmer D. Introducing Sol: A General Methodology for Equation-Based Modeling of Variable-Structure Systems. Proc. 6th International Modelica Conference, Bielefeld, Germany, Vol.1 47-56, 2008.
Zimmer Dirk. Multi-Aspect Modeling in Equation-Based Languages // Journal «Simulation News Europe», Volume 18, Number 2, August 2008, ISSN 0929-2268. P.54-62.
Current equation-based modeling languages are often confronted with tasks that partly diverge from the original intended application area. This results out of an increasing diversity of modeling aspects. This paper briefly describes the needs and the current handling of multi-aspect modeling in different modeling languages with a strong emphasis on Modelica. Furthermore a small number of language constructs is suggested that enable a better integration of multiple aspects into the main-language. An exemplary implementation of these improvements is provided within the framework of Sol, a derivative language of Modelica.