Abrahamson D. (2009). A student's synthesis of tacit and mathematical knowledge as a researcher's lens on bridging learning theory // In M. Borovcnik & R. Kapadia (Eds.), Research and developments in probability education [Special Issue]. International Electronic Journal of Mathematics Education, 4(3), pp.195-226.

Abrahamson D. (2009). Embodied design: Constructing means for constructing meaning // Educational Studies in Mathematics, 70(1), pp.27-47.

Aksyonov K.A., Bykov E.A., Aksyonova O.P., Wang Kai, Popov A.V., Smoliy E.F., and others. Development of Decision Support and Simulation System BPsim.DSS: Integration of Simulation, ex-pert Situational and Multi-Agent Modeling // Proceedings of ESM'2009 (ESM - European Simulation and Modelling Conference) October 26-28, 2009. Holiday Inn, Leicester, United Kingdom. p.256-260.
Разработка системы поддержки принятия решений «BPsim.DSS»: интеграция имитационного, экспертного, ситуационного и мультиагентного моделирования.

Aksyonov K.A., Bykov E., Smoliy E., Khrenov A., Kolosov D. Multi-Agent Resource Conversion Processes Simulation // International journal of information and systems sciences. 2009. Vol. 5. №2. р. 260-269.
Имитационное моделирование мультиагентных процессов преобразования ресурсов.

Aksyonov K.A., Bykov E.A., Smoliy E.F., Popov M.V. Multi-service communication networks simulation and design with BPsim3 // Proceedings of the 2009 Winter Simulation Conference (WSC 2009), December 13-16, 2009. Austin, Texas, USA. 2009. р.2768-2777.
Проектирование и имитационное моделирование мультисервисных сетей связи в BPsim3.

Aksyonov K.A., Bykov E., Wang Kai, Smoliy E., Aksyonova O. Multi-Agent Processes Simulation with BPsim.MAS — An Easy Way to Success // Proceedings of the IEEE 2009 Chinese Control and Decision Conference (CCDC 2009), 17-19 June 2009. Guilin, China. р.5661-5666.
Имитационное моделирование мультиагентных процессов с BPsim.MAS - легкий путь к успеху.

Aksyonov K.A., Smoliy E.F., Bykov E.A., Popov M.V., Dorosinskiy L.G. Development of decision support system «BPsim3»: Multi-service telecommunication networks design and modeling application // Proceedings of 10th International PhD Workshop on Systems and Control, Hluboka nad Vltavou. Czech Republic, 2009. р.112-117.
Разработка системы поддержки принятия решений «BPsim3»: применение к задачам моделирования и проектирования мультисервисных сетей связи.

Aksyonov K.A., Spitsina I., Bykov E., Smoliy E., Aksyonova O. Computer-supported software development with BPsim products family – integration of multiple approaches // Proceedings of the 2009 IEEE International Conference on Information and Automation (ICIA), 22-25 June 2009. Zhuhai/Macau, China. 2009. р.1532-1536.
Автоматизированная разработка программного обеспечения на основе продуктов семейства BPsim - интеграция подходов.

Aksyonov K.A., Spitsina I., Bykov E., Wang Kai, Smoliy E. Multiple Approaches Integration for Computer-Supported Software Development // Proceedings of the IEEE 2009 Chinese Control and Decision Conference (CCDC 2009), 17-19 June 2009. Guilin, China. р.4939-4943.
Интеграция сложных подходов для автоматизированной разработки программного обеспечения.

An G., Wilensky U. (2009). From artificial life to in silico medicine: NetLogo as a means of translational knowledge representation in biomedical research // In A. Adamatzky & M. Komosinski (Eds.), Artificial Life Models in Software (2nd Ed.). Berlin: Springer-Verlag.

Anfilets S.V., Shut V.N. The creation of models of adjustable crossroads on GPSS // Proceedings of the 9th International Conference «Reliability and Statistics in Transportation and Communication» (RelStat’09), 21–24 October 2009, Riga, Latvia, p.433-438. ISBN 978-9984-818-21-4.
The example of modelling of crossroads on GPSS (General Purpose Simulation System) is resulted in the article. By means of GPSS and language PLUS it is possible to develop models of rigid management, adaptive management and the mixed scheme of management. The developed models of rigid and adaptive management are compared on various streams. Models of crossroads in the subsequent can be connected in the certain scheme, characteristic a street network of a city, to carry out the analysis of work of whole city scheme with regulation on the basis of adaptive management, rigid management or the mixed scheme.

Ang C.S., Zaphiris P. (2009). Simulating Social Networks of Online Communities: Simulation as a Method for Sociability Design // In Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II (INTERACT '09), Tom Gross, Jan Gulliksen, Paula Kotzé, Lars Oestreicher, Philippe Palanque, Raquel Oliveira Prates, and Marco Winckler (Eds.). Springer-Verlag, Berlin, Heidelberg, pp.443-456.

BenDor T., Westervelt J., Aurambout J., Meyer W. (2009). Simulating population variation and movement within fragmented landscapes: An application to the gopher tortoise (Gopherus polyphemus) // Ecological Modelling 220(6), pp.867-878.

Berryman Matthew J., Angus Simon D. (2009). Chapter 1. Tutorials on Agent-based modelling with NetLogo and Network Analysis with Pajek // World Scientific Review. October 12, 2009.
This chapter is aimed at beginners to complex systems modelling and network analysis, using NetLogo (Section 1.1) and Pajek (Section 1.2) respectively. It is also aimed at more advanced complex systems modellers who want an introduction to these platforms.

Blikstein P., Wilensky U. (2009). An atom is known by the company it keeps: A constructionist learning environment for materials science using multi-agent simulation // International Journal of Computers for Mathematical Learning, 14(1), pp.81-119.
This article reports on «MaterialSim», an undergraduate-level computational materials science set of constructionist activities which we have developed and tested in classrooms.

Blikstein P., Wilensky U., Abrahamson D. (2009, April). Towards a framework for cognitive research using agent-based modeling and complexity sciences // In M. Jacobson (Chair), M. Kapur (Organizer) & N. Sabelli (Discussant), Complexity, learning, and research: Under the microscope, new kinds of microscopes, and seeing differently. Symposium conducted at the annual meeting of the American Educational Research Association, San Diego, CA.

Brodsky Yury I. Simulation Software //System Analysis and Modeling of Integrated World Systems - Volume 1, Oxford: EOLSS Publishers Co. Ltd., 2009, p. 287-298.

Brodsky Yury I., Tokarev Vladislav V. Fundamentals of simulation for complex systems // System Analysis and Modeling of Integrated World Systems - Volume 1, Oxford: EOLSS Publishers Co. Ltd., 2009, p. 235-250.

J. Ethan Brown, David Sturrock Identifying cost reduction and performance improvement opportunities through simulation // Proceedings of the 2009 Winter Simulation Conference M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin and R. G. Ingalls, eds. P.2145-2153.
During difficult economic times, companies have few positive cost reducing options that simultaneously improve operational performance. This paper addresses how Deloitte Consulting partnered with Simio LLC to model multiple process improvement opportunities for a HVAC manufacturer in order to reduce the facility’s operating costs. Through the use of simulation, the team was able to determine the impact of reducing the cost burden for the HVAC company by minimizing WIP inventory, eliminating over-time labor and increasing throughput. Four separate improvement opportunities were modeled independently and conjointly to provide insight into the size of the savings opportunities as well as to enable the prioritization of those efforts.

Cao L., Gorodetsky V., Mitkas P.A. Agent mining: the synergy of agents and data mining // International Journal IEEE Intelligent Systems. 2009. Vol. 24. No. 3. P. 64-72.

Pau Fonseca i Casas, Josep Casanovas. JGPSS, an open source GPSS framework to teach simulation // Proceedings of the 2009 Winter Simulation Conference. P.256–267.
GPSS has been used for years to teach simulation. Different tools following the GPSS syntax exist. Usually these tools can be used to construct simulation models helping in the teaching of simulation. However no available framework is capable to simplify the development of a complete simulation tool following the GPSS syntax. This paper presents JGPSS, a framework based in Java language that can be used by students to build a complete simulation tool following the GPSS syntax. JGPSS has two versions, one capable to perform simulations and obtain statistical information, and other designed as a framework for students of computer sciences. This last framework, described in this paper, lacks in the implementation of the main simulation engine algorithms or the related structures. This framework allows the computer science student to reach a deeper understanding of how to construct a complete process interaction simulation engine, the paradigm that GPSS follows.

Carmichael T., Hadzikadic M., Dreau D., Whitmeyer J. (2009). Characterizing Threshold Effects Across Diverse Phenomena // In Z. Ras & W. Ribarsky (Eds), Advances in Information and Intelligent Systems. New York: Springer.

Castillo L.F., Bedia M.G., Uribe A.L., Isaza G. (2009). A formal approach to test commercial strategies: Comparative study using Multi-agent based techniques // Journal of Physical Agents, 3(3), 25-30.(1), pp.27-47.

Chau K.Y., Liu S.B., Lam C.Y. Multi-Agent Modeling in Managing Six Sigma Projects // International Journal of Engineering Business Management, Vol. 1, No. 1 (2009), pp. 9-14.
In this paper, a multi-agent model is proposed for considering the human resources factor in decision making in relation to the six sigma project. The proposed multi-agent system is expected to increase the acccuracy of project prioritization and to stabilize the human resources service level. A simulation of the proposed multiagent model is conducted. The results show that a multi-agent model which takes into consideration human resources when making decisions about project selection and project team formation is important in enabling efficient and effective project management. The multi-agent modeling approach provides an alternative approach for improving communication and the autonomy of six sigma projects in business organizations.

Chu C.J., Weiner J., Maestre, F.T., Xiao S., Wang Y.S., Li Q., Yuan J.L., Zhao L.Q., Ren Z.W., Wang W. (2009). Positive interactions can increase size inequality in plant populations // Journal of Ecology 94: 1401-1407.

Cicirelli F., Furfaro A., Giordano A., Nigro L. Distributing repast simulations using actors // Proceedings 23rd European Conference on Modelling and Simulation ECMS Javier Otamendi, Andrzej Bargiela, Jose Luis Montes, Luis Miguel Doncel Pedrera (Editors). 2009. ISBN: 978-0-9553018-8-9 / ISBN: 978-0-9553018-9-6 (CD).
This paper describes an approach aimed to distributing RePast models, with minimal changes, over a networked context so as to address very large and reconfigurable models whose computational needs (in space and time) can be difficult to satisfy on a single machine.

Deviatkov V.V. Vlasov S.A., Deviatkov T.V. Creation Principals of Universal Modeling Environment for Simulation Application Development (Принципы разработки универсальной моделирующей среды при создании имитационных приложений) // Preprints 13th IFAC Symposium on Information Control Problems in Manufacturing (INCOM`09), Volume 13. – Part 1 **. – Moscow (Russia): IFAC, June 3-5, 2009. – P. 1814-1819.

Drachsler H., Hummel H., van den Berg B., Eshuis J., Waterink W., Nadolski R., Berlanga A., Boers N., Koper R. (2009). Evaluating the Effectiveness of Personalised Recommender Systems in Learning Networks // In R. Koper (ed.), Learning Network Services for Professional Development (pp.95-113), Berlin:Springer-Verlag.

Dreau D., Stanimirov D., Carmichael T., Hadzikadic M. (2009). An agent-based model of solid tumor progression // Paper presented at the 1st International Conference on Bioinformatics and Computational Biology, New Orleans, LA., April 2009.

Filatova T., Parker D., van der Veen A. (2009). Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change // Journal of Artificial Societies and Social Simulation (JASSS), 12 (1): 3.

Friedman D., Abraham R. (2009). Bubbles and crashes: Gradient dynamics in financial markets // Journal of Economic Dynamics and Control 33(4), pp.922-937.

Galan Jose Manuel; Izquierdo Luis; Izquierdo Segismundo S.; Santos Jose Ignacio; del Olmo Ricardo; Lopez-Paredes Adolfo; Edmonds Bruce (2009). Errors and artefacts in agent-based modelling // Journal of Artificial Societies and Social Simulation. 12 (1): 1.

Graham S. (2009). Behaviour Space: Simulating Roman Social Life and Civil Violence. Digital Studies / Le Champ NuméRique, 1(2).

Gulyas Laszlo; Szemes Gábor; Kampis George; de Back Walter (2009). A modeler-friendly API for ABM Partitioning // Proceedings of the ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2009. San Diego, California, USA. 2: 219–226.

Hadzibeganovic Tarik; Stauffer Dietrich; Schulze Christian (2009). Agent-based computer simulations of language choice dynamics // Annals of the New York Academy of Sciences. 1167 (1): 221–229. doi:10.1111/j.1749-6632.2009.04507.x.

Handel A., Yates A., Pilyugin S.S., Antia R. (2009). Sharing the burden: Antigen transport and firebreaks in immune responses // Journal of the Royal Society Interface 6, pp.447-454.

Hosseinpour F., Hajihosseini H. Importance of Simulation in Manufacturing // World Academy of Science, Engineering and Technology. Vol 3, № 3, 2009. P.261-264.
Simulation is a very helpful and valuable work tool in manufacturing. It can be used in industrial field allowing the system`s behavior to be learnt and tested. Simulation provides a low cost, secure and fast analysis tool. It also provides benefits, which can be reached with many different system configurations. Topics to be discussed include: Applications, Modeling, Validating, Software and benefits of simulation. This paper provides a comprehensive literature review on research efforts in simulation.

Hunt C.A., Ropella G.E.P., Lam T.N., Tang J., Kim S.H. J., Engelberg J.A., Sheikh-Bahaei S. (2009). At the biological modeling and simulation frontier // Pharmaceutical Research 26(11), pp.2369-2400.

Ivanov D.A., Sokolov B.V., Kaeschel J. A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations // European Journal of Operational Research, 2009.
A trend in up-to-date developments in supply chain management (SCM) is to make supply chains more agile, flexible, and responsive. In supply chains, different structures (functional, organizational, informational, financial etc.) are (re)formed. These structures interrelate with each other and change in dynamics. The paper introduces a new conceptual framework for multi-structural planning and operations of adaptive supply chains with structure dynamics considerations. We elaborate a vision of adaptive supply chain management (A-SCM), a new dynamic model and tools for the planning and control of adaptive supply chains. SCM is addressed from perspectives of execution dynamics under uncertainty.

Ivashkin Y.A., Nazoikin Е.А. Agent-based simulation model of educational process in the student group // International Conference on Computational Intelligence, Modelling and Simulation - Brno, Czech Republic, 2009. P. 132-137.

Izquierdo L.R., Izquierdo S.S., Galán J.M., Santos J.I. (2009). Techniques to Understand Computer Simulations: Markov Chain Analysis // Journal of Artificial Societies and Social Simulation (JASSS), 12 (1): 6.

Janssen M.A. (2009). Understanding Artificial Anasazi // Journal of Artificial Societies and Social Simulation (JASSS), 12 (4): 13.

Jenkins Charles M., Rice Stephen V. Resource modeling in discrete–event simulation environments: a fifty–year perspective // Proceedings of the 2009 Winter Simulation Conference M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin and R. G. Ingalls, eds. С.755-766.
Through 50 years of innovation, discrete–event simulation environments have offered similar approaches to modeling the re-sources that participate in simulations. These approaches involve «clients» and «servers» of varying activity levels that wait in queues of varying sophistication. While powerful enough for many applications, these models limit the complexity of the entities that may be represented. Analysis of more than thirty simulation environments provides the substrate for defining «levels» of modeling features from primitive foundations to advanced embellishment. This analysis not only supports comparison of existing resource models, but also informs the development of new approaches.

Jepsen M.R., Simonsen J., Ethelberg S. (2009) Spatio-temporal cluster analysis of the incidence of Campylobacter cases and patients with general diarrhea in a Danish county // International Journal of Health Geographics, 8(11), pp. 1-12.

Johnson B.R. (2009). A self-organizing model for task allocation via frequent task quitting and random walks in the honeybee // American Naturalist 174, pp.537-547.

Jurenoks V., Jansons V., Didenko K. Investigation of Economic Systems using Modelling Methods with Copula // XI International Conference on Computer Modelling and Simulation UKSim 2009, March 25–27, 2009, Cambridge, United Kingdom, 2009, p.311-316.

Kimbrough S.O., Murphy F.H. (2009). Learning to collude tacitly on production levels by oligopolistic agents // Computational Economics 33(1), pp.47-78.

Kincaid J. P., Westerlund K.K. Simulation in education and training // Proceedings of the 2009 Winter Simulation Conference. P. 273-280.

Kontoyiannakis K., Serrano E., Tse K., et al. A simulation framework to evaluate airport gate allocation policies under extreme delay conditions // Processing of the Winter Simulation Conference (13–16 Dec. 2009, Austin) / Ed. by M.D. Rossetti, R.R. Hill, B. Johansson, A. Dunkin, R.G. Ingalls. / Piscataway (New Jersey): Inst. Of Electric. and Electron. Engrs, 2009, p. 2332–2342.

Kornhauser D., Wilensky U., Rand W. (2009). Design guidelines for agent based model visualization // Journal of Artificial Societies and Social Simulation, JASS, 12(2), 1.
This paper provides agent-based modeling (ABM) visualization design guidelines in order to improve visual design with ABM toolkits. These guidelines will assist the modeler in creating clear and understandable ABM visualizations.

Krahl David ExendSim advanced technology: discrete rate simulation // In: Proceeding of the 2009 Winter Simulation Conference, 2009. pp. 333-338.

Lansing J.S., Cox M.P., Downey S.S., Janssen M.A., Schoenfelder J.W. (2009). A robust budding model of Balinese water temple networks // World Archaeology 41(1), pp.112-133.

Averill M. Law How to build valid and credible simulation models // Proceedings of the 2009 Winter Simulation Conference M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin and R. G. Ingalls, eds. P.24-33.
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 real-world examples. We will also discuss the difficulty in using formal statistical techniques (e.g., confidence intervals) to validate simulation models.

Lerner R., Levy S.T., Wilensky U. (2009). Design of the Modeling Commons // Chais Conference, Tel Aviv, Israel.

Levy S.T., Wilensky U. (2009). Crossing levels and representations: The Connected Chemistry (CC1) curriculum // Journal of Science Education and Technology, 18(3), pp.224-242.
Connected Chemistry (named CC1 to denote Connected Chemistry Chapter 1) is a computer-based environment for learning the topics of gas laws and kinetic molecular theory in chemistry. It views chemistry from an «emergent» perspective, how macroscopic phenomena result from the interaction of many submicroscopic particles. Connected Chemistry employs agent-based models built in NetLogo, embedded in scripts that structure and log the students’ activities.

Levy S.T., Wilensky U. (2009). Students' learning with the Connected Chemistry (CC1) curriculum: Navigating the complexities of the particulate world // Journal of Science Education and Technology, 18(3), pp.243-254.

Linard C., Ponçon N., Fontenille D., Lambin E.F. (2009). A multi-agent simulation to assess the risk of malaria re-emergence in southern France // Ecological Modelling, 220(2): 160-174.

Liu B., Liu Z., Hong Y. A simulation based on emotions model for virtual human crowds // 2009 Fifth International Conference on Image and Graphics. IEEE. 2009. P. 836-840. DOI: 10.1109/ICIG.2009.24.

Emmanuel López-Neri Microscopic discrete event urban traffic model validation using simulation // Proceedings of the 9th International Conference «Reliability and Statistics in Transportation and Communication» (RelStat'09), 21-24 October 2009, Riga, Latvia, p. 384-393. ISBN 978-9984-818-21-4. P.384-393.
In this paper a microscopic discrete event urban traffic model validation using simulation is presented. In a previous study a hierarchical microscopic urban traffic system (UTS) model was developed. That model integrates the event oriented and agent-based approach. The UTS is described using the multi-level Petri net based formalism, named n-LNS. Usually simulators are designed using time step approach and are validated using real data and is verified that the flow/density relationship (fundamental diagram) are conserved and then state the simulator generates a valid behavior. However, the model used in this paper uses the event oriented approach, doing more complex the process to obtain these validation graphs and their corresponding analysis. In order to validate it, was developed a library known as CiudadelaSim.

Natalia N. Lychkina, Dmitriy Shults. Simulation modeling of regions` social and economic development in decision support systems. Компьютерное моделирование социально-экономического развития регионов в системах поддержки принятия решений // Материалы 27-ой Международной конференции Общества системной динамики, США, Альбукерке, Нью Мехико, 26-30 июля 2009 г., System Dynamics.

Maharaj S., McCaig C., Shankland C.E. (2009). Studying the effects of adding spatiality to a process algebra model // 8th Workshop on Process Algebra and Stochastically Timed Activities: 153-158, 2009. Edinburgh.

Malykhanov A.A., Chernenko V.Е. Extensible Framework for Microscopic Traffic Simulation. Proceedings of the 2009 Winter Simulation Conference (Ph. D. Colloquium). Austin, TX, USA. ISBN: 978-1-4244-5772-4.

Malykhanov А.А., Kumunjicv K.V., Chernenko V.E. Modeling Driver Behavior for Microscopic Traffic Simulation // Interactive Systems and Technologies: the Problems of Human-Computer Interaction. Volume III. - Collection of scientific papers. Ulyanovsk: ULSTU, 2009. - P. 402 – 407.

Manzo G. (2009). Boudon’s Model of Relative Deprivation Revisited // In Cherkaoui, M. & Hamilton, P. (eds.) Raymond Boudon: A Life in Sociology, Oxford, Bardwell Press, vol. 3, part 3, ch. 46, pp.91-121.

Masloboev A.V. Application of system dynamics and agent based tech nologies for innovative processes development simulation // Качество. Инновации. Образование, №3 / 2009, p. 34-42.
In this paper the problem of information support of innovation management is considered. The importance of computer modeling in solving of the problem is represented A short description of system dynamics method and multi-agent technology as efficient means for complex system simulation is given. The system dynamic and agent-based models structure of market innovation distributions (diffusion) are proposed. The scientific-methodical foundations of multi-agent technologies and system dynamics method integration are developed.

Yuri Merkuryev, Galina Merkuryeva, Roel De Haes, Bram Desmet, An De Wispelaere, Jonas Hatem. Supply chain simulation in the ECLIPS project: Real-life benefit. // AMS 2009, Asia Modelling Symposium 2009, Third Asia International Conference on Modelling and Simulation, 25-26/29 May 2009, Bandung/Bali, Indonesia. Ed. by David Al-Dabass, Robertus Triweko, Sani Susanto, and Ajith Abraham. – IEEE, IEEE Computer Society, 2009. P. 526-532.

Moemeng Ch., Gorodetsky V., Cao L. Agent-Based distributed data mining: A Survey // Data Mining and Multi-agent Integration. L .Cao (Ed.). Springer, 2009. P. 47-58.

Monks Thomas, Robinson Stewart, and Kotiadis Kathy (2009). Model reuse versus model development: effects on credibility and learning. In: Winter Simulation Conference (WSC ‘09). Winter Simulation Conference, pp. 767-778.

Nadolski R.J., van den Berg B., Berlanga A.J., Drachsler H., Hummel H.G.K., Koper R., Sloep P.B. (2009). Simulating Light-Weight Personalised Recommender Systems in Learning Networks: a Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies // Journal of Artificial Societies and Social Simulation (JASSS), 12(1).

Niazi M., Hussain A. (2009). Agent-Based Tools for Modeling and Simulation of Self-Organization in Peer-to-Peer, Ad Hoc, and Other Complex Networks // IEEE Communications Magazine, Vol.47 No.3.

Niazi M., Hussain A., Kolberg M. (2009). Verification and Validation of Agent-Based Simulations using the VOMAS approach // Proceedings of the Third Workshop on Multi-Agent Systems and Simulation '09, as part of MALLOW 09, Sep 7-11, 2009, Torino, Italy.

Novak M., Levy S.T., Wilensky U. (2009). Playing in a particle sandbox and gaining a glass box perspective: The Connected Chemistry curriculum // Working paper.

Oren T.I. Zeigler Concepts for Advanced Simulation Methodologies, Simulation / T. I. Oren, B. P. Zeigler. – North-Holland Publishing company, 2009. – Рp. 78 – 88.

O'Sullivan D. (2009). Changing neighborhoods - Neighborhoods Changing; A Framework for spatially explicit agent-based models of social systems // Sociological Methods Research, 37(4), pp.498-530.

Patarakin E., Yarmakhov B. (2009). Modeling organizational relations with the NetLogo «links» Educational Technology & Society // Educational Technology & Society, No. 2. pp. 409-422. (In Russian).

Perez L., Dragicevic S. An agent-based approach for modeling dynamics of contagious disease spread. International Journal of Health Geographics, 2009, no. 8 (50). DOI: 10.1186/1476-072X-8-50.

Reindl S., M¨onch M., M¨onch L., Scheider A. Modeling and simulation of cataract surgery processes // Processing of the Winter Simulation Conference (13–16 Dec. 2009, Austin)/ Ed. by M.D. Rossetti, R.R. Hill, B. Johansson, A. Dunkin, R.G. Ingalls. / Piscataway (New Jersey): Inst. of Electric. and Electron. Engrs, 2009, p. 1936–1945.

Rolon M., Canavesio M., Martínez E. (2009). Agent Based Modelling and Simulation of Intelligent Distributed Scheduling Systems // In Jezowski Jacek & Thullie Jan (Eds.), Proceedings of the 19th European Symposium on Computer Aided Process Engineering (Vol. 26, pp. 985-990). Amsterdam, Holland: Elsevier.

Rolon M., Canavesio M., Martinez E. (2009). Generative Modeling of Holonic Manufacturing Execution Systems for Batch Plants // In R.M. de Brito Alves, C.A.O. do Nascimento , & E.C. Biscaia Jr. (Eds.),10th International Symposium on Process Systems Engineering: Part A (Vol. 27, 795-800). Amsterdam, Holland: Elsevier.

Rossetti M.D., Hill R.R., Johansson B., et al. Sequential metamodelling with genetic programming and particle swarms // Processing of the Winter Simulation Conference (13–16 Dec. 2009, Austin)/ Ed. by M.D. Rossetti, R.R. Hill, B. Johansson, A. Dunkin, R.G. Ingalls. / Piscataway (New Jersey): Inst. of Electric. and Electron. Engrs, 2009.

Sakellariou I., Kefalas P., Stamatopoulou I. (2009).MAS Coursework Design in NetLogo // In Proceedings of the Educational Uses of Multi Agent Systems, Budapest, Hungary.
In the context of an Intelligent Agents course, we have chosen NetLogo as the means to satisfy the students' demand for hands-on practice, to help them understand at a deeper level the otherwise theoretical aspects involved in the design of a multi-agent system (MAS). In this paper we present in detail the structure of the two pieces of coursework assigned to the students, the first one introducing students to the reactive architecture and the second, building on the first, to the hybrid architecture, also incorporating agent communication issues and interaction protocols.

Savrasovs, M. Overview of traffic mesoscopic models // In: The 2nd International Magdeburg Logistics PhD Student Workshop, 2009. Magdeburg, 2009, pp. 71-79.

Schenk M., Tolujew J., Reggelin T. Mesoscopic Modeling and Simulation of Logistics Networks // Preprints of the 13th IFAC Symposium on Information Control Problems in Manufacturing. — Moscow, Russia, June 3–5, 2009. — P. 586–591.

Sakellariou I., Kefalas P., Stamatopoulou I. (2009).MAS Coursework Design in NetLogo // In Proceedings of the Educational Uses of Multi Agent Systems, Budapest, Hungary.
In the context of an Intelligent Agents course, we have chosen NetLogo as the means to satisfy the students' demand for hands-on practice, to help them understand at a deeper level the otherwise theoretical aspects involved in the design of a multi-agent system (MAS). In this paper we present in detail the structure of the two pieces of coursework assigned to the students, the first one introducing students to the reactive architecture and the second, building on the first, to the hybrid architecture, also incorporating agent communication issues and interaction protocols.

Serova E.G. The role of Multi-agent Approach in Building Information Infrastructure for a Modern Company and Carrying Out Management Tasks // International book series «Information Science and Computing», Intelligent Information and Engineering Systems, Vol.3. 2009.

Sha O.P., Misra S.C., Gupta Ashish. Simulation of Block Assembly Process in Shipbuilding by Petri-nets // WMTC 2009.
The paper discusses how the simulation of the two and three block assembly process exercise is helpful in locating the bottle-necks over longer time span of the production process and at what rate the blocks can be accepted at the erection stage of shipbuilding. In order to achieve faster production it is necessary to reduce the occurrence as well as time span of bottlenecks. Simulation of multiple block assembly process also gives an idea whether the present assembly area is adequate to meet the production schedule.

Sharma S. Simulation and modeling of group behavior during emergency evacuation // 2009 IEEE Symposium on Intelligent Agents. IEEE. 2009. P. 122-127. DOI: 10.1109/IA.2009.4927509.

Shi J., Ren A., Chen C. Agent-based evacuation model of large public buildings under fire conditions // Automation in Construction. 2009. Vol. 18, Is. 3. P. 338-347. DOI: 10.1016/j.autcon.2008.09.009.

Shultz L. (2009). Understanding the Greenhouse Effect Using a Computer Model // Unpublished Master's thesis, University of Maine.

Siddiqa A., Niazi M., Mustafa F., Bukhari H., Hussain A., Akram N., Shaheen S., Ahmed F., Iqbal S. (2009). A New Hybrid Agent-Based Modeling & Simulation Decision Support System For Breast Cancer Data Analysis // IEEE ICICT 09, IBA, Karachi.

Sokolov Boris.V., Ivanov D.A., Verzilin D.N., Zaychik E.M. Parametric adaptation of models describing structure-dynamics control processes in complex technical systems (CTS) // 23rd European Conference on Modelling and Simulation (June, 9th–12th,, 2009, Madrid, Spain).

Steiniger S., Hay G.J. (2009). Free and open source geographic information tools for landscape ecology // Ecological Informatics 4(4), pp.183-195.

Stonedahl F., Wilkerson-Jerde M., Wilensky U. (2009). Re-conceiving introductory computer science curricula through agent-based modeling // Paper presented at the Eighth International Conference on Autonomous Agents and Multi-agent Systems (AAMAS) – EduMAS Workshop, Budapest, Hungary.
We present a preliminary version of the MAICS (Multi-Agent Introduction to Computer Science) framework, which is a new approach for revitalizing introductory undergraduate or high school computer science curricula through the deep integration of agent-based modeling and multi-agent systems perspectives. We have developed a suite of educational agent-based models highlighting several key ideas of computer science.

David T. Sturrock Tips for successful practice of simulation // Proceedings of the 2009 Winter Simulation Conference M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin and R. G. Ingalls, eds.
A simulation project is much more than building a model. And the skills required go well beyond knowing a particular simulation tool. This paper discusses some important steps to enable project success and some cautions and tips to help avoid common traps.

Sudhira H.S., Ramachandra T.V. (2009). A Spatial Planning Support System for Managing Bangalore’s Urban Sprawl // In Stan Geertman and John Stillwell (Eds.), Planning Support Systems: Best Practice and New Methods. Springer.

Tako A.A. and Robinson S. 2009. Comparing discrete-event simulation and system dynamics: users' perceptions. Journal of the Operational Research Society, 60:296-312.

Vasileva, S. Modeling of Distributed Transactions with Priorities. // Third International Conference on Information Systems & GRID Technologies, 28 - 29 May 2009, Sofia, Bulgaria, organized by University of Sofia «St. Kliment Ohridski» and BulAIS - Bulgarian Chapter of AIS, Dedicated to 120 years of Faculty of Mathematics and Informatics at University of Sofia, Sofia, 2009, pp.86-95.

Vasileva, S., A. Milev. Modeling of Two-version Algorithms for Two-Phase Locking in Distributed Databases. // International Conference Automatics and Informatics’09, 3 – 4 October 2009, Sofia, Bulgaria, pp. I-13 – II-16.

Vattam S., Goel A.K., Rugaber S., Hmelo-Silver C., Jordan R. (2009). From Conceptual Models to Agent-based Simulations: Why and How // Paper presented at the 14th International Conference on Artificial Intelligence in Education (AIED), 2009.

Verma, R., Gupta, A., Singh, K. A critical evaluation and comparison of four manufacturing simulation software // Kathmandu University Journal of Science, Engineering and technology, 5(1), 2009. pp. 104–120.

Vlasov S.A., Deviatkov V.V., Deviatkov T.V. Creation Principals of Universal Modeling Environment for Simulation Application Development// 131'1 IF AC Symposium on Information Control Problems in Manufacturing. 2009. Moscow. Russia.

Volskov D.G.. Rivin G.L. Visualization of processes of work of the airport on the basis of system of mass service and the computerization of management of flights // Известия Самарского научного центра Российской академии наук, т. 11. Механика и машиностроение. №3(2), 2009. P.424–429.
In clause authors consider model of approach of air courts to the airport in program Blender which allows to predict situations and to prompt the aviadispatcher those or other possible actions on management of an air vessel. Authors use the theory of mass service on the basis of which form results of researches of the phenomena arising in systems of service of an air vessel.

Weisberg M., Muldoon R. (2009). Epistemic Landscapes and the Division of Cognitive Labor. Philosophy of Science, 76, pp.225-252.

Wilkerson M. (2009). Agents with attitude: Exploring Coombs Unfolding technique with agent-based models // International Journal of Computers for Mathematical Learning, 14 (1), pp.51-60.
In this paper, I briefly introduce Coombs unfolding technique, and model the technique using simple agent-based models (ABMs) developed in the NetLogo modeling environment. I then attempt to address some of the difficulties one may encounter when applying Coombs unfolding to a population by using these ABMs as a starting point. Finally, I discuss the affordances and disadvantages of using computational modeling as a starting point for thinking about applied mathematical problems.

Wilkerson-Jerde M., Wilensky U. (2009, April). Complementarity in agent-based and equation-based models // Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA.

Irina Yatskiv, Mihail Savrasov Riga-Minsk transport corridor simulation model development // Proceedings of the 9th International Conference «Reliability and Statistics in Transportation and Communication» (RelStat’09), 21–24 October 2009, Riga, Latvia, p. 394-403. ISBN 978-9984-818-21-4.
This paper deals with the developed cargo traffic macroscopic simulation model. The goal of model development is to analyse and study Riga-Mink transport corridor. The simulation model was developed using specialised simulation software called PTV VISION VISUM. VISUM uses the transport model that defines requirement for input data. The developed model consists of transport network model and demand model. The demand model is presented by two origin destination matrices, which have been calibrated using TFlowFuzzy algorithm. Further calibrated model has been used to estimate two development scenarios using different output data of VISUM.

Yi S., Shi J. An agent-based simulation model for occupant evacuation under fire conditions // 2009 WRI Global Congress on Intelligent Systems. IEEE, 2009. Vol. 1. P. 27-31. DOI: 10.1109/GCIS.2009.442.

Elena Yurshevich, Aleksandra Kalna, Andris Kalns Application of the agent-based simulation approach to the bank department functioning analysis // Proceedings of the 9th International Conference «Reliability and Statistics in Transportation and Communication» (RelStat’09), 21–24 October 2009, Riga, Latvia, p. 404-412. ISBN 978-9984-818-21-4.
This paper deals with the results of agent-based simulation approach application in the field of process management. The model has been implemented on the base of simulation modelling tool AnyLogic. The results of experimentation allow defining different options of working process organization, which give the capability to reduce expenses and to improve efficiency of work.

Zakerifar, M., Biles, E.W., Evans, W.G. Kriging metamodeling in multi-objective simulation optimization // In Proceedings of the 2009 Winter Simulation Conference, IEEE, Inc., Austin, TX, USA, pp. 2115–2122.

Zhai Z., Schoenharl T., Chen F., Madey G. (2009). Design and Implementation of an Agent-Based Simulation for Emergency Response and Crisis Management // Department of Computer Science and Engineering, University of Norte Dame, IN.