A Hybrid Simulation Approach to Dynamic Multi-Skilled Workforce Planning of Production Line Yuan Feng, Wenhui Fan, Tsinghua University. Winter Simulation Conference, 2014.

Workers cross-trained with multiple tasks can improve the workforce flexibility for the plant to handle
variations in workload. Therefore, it is necessary to study the dynamic multi-skilled workforce planning
problem of production line with the application of cross-training method.
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Electric Vehicle Driver Simulation using Agent-Based Modeling Beaudry Kock, Recargo, Inc., 2014

Recargo has been developing an agent-based model with the AnyLogic tool to help us simulate the charging patterns of electric vehicle drivers in California. Our goal is to better understand the potential value from delivering electricity grid services with these vehicles. Development has only been underway for a few weeks, but in that time we’ve been able to use AnyLogic’s accessible interface and Java coding tools to quickly build and test a proof-of-concept model with which we can explore the potential for a more sophisticated and complex effort.
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A Generic Operational Simulation for Early Design Civil Unmanned Aerial Vehicles Benjamin Schumann, Jim Scanlan (School of Engineering Sciences University of Southampton); Kenji Takeda (Microsoft Research). Proceedings of the SIMUL 2011: The Third International Conference on Advances in System Simulation

Contemporary aerospace programmes often suffer from large cost overruns, delivery delays and inferior product quality. This is caused in part by poor predictive quality of the early design phase processes with regards to the operational environment of a product. This paper develops the idea of a generic operational simulation that can help designers to rigorously analyse and test their early product concepts. The simulation focusses on civil Unmanned Air Vehicle products and missions to keep the scope of work tractable.
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AnyLogic 4.0: Simulating Hybrid Systems with Extended UML-RT Andrei Borshchev. Simulation News Europe, No. 31 April 2001, pp 15-16

We outline a modelling approach aimed to capture sophisticated interdependencies of discrete and continuous behaviors in hybrid systems. The approach is essentially a hybrid extension of widely recognized object-oriented languages UML and UML-RT. It is fully supported by a new simulation tool AnyLogic 4.0 from Experimental Object Technologies.
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Decision Support Tool — Supply Chain Christian Wartha, Momtchil Peev, Andrei Borshchev, and Alexei Filippov. 2002 Winter Simulation Conference (WSC’02), December 8-11, 2002, San Diego, California, USA

We present a currently developed Decision Support Tool - Supply Chain (DST-SC). This is specialized domain oriented tool, which is an extension of the general purpose, UML-RT Hybrid Simulation kernel of AnyLogic by XJ Technologies. DST-SC allows high degree of flexibility with respect to the supply chain functionality being modeled, has the ability to handle large complex problems, and offers highly reusable model components, offering at the same time ease of use by non-experts in simulation.
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From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools Andrei Borshchev and Alexei Filippov. The 22nd International Conference of the System Dynamics Society, July 25 - 29, 2004, Oxford, England

This paper may be considered as a practical reference for those who wish to add (now sufficiently matured) Agent Based modeling to their analysis toolkit and may or may not have some System Dynamics or Discrete Event modeling background. We focus on systems that contain large numbers of active objects (people, business units, animals, vehicles, or even things like projects, stocks, products, etc. that have timing, event ordering or other kind of individual behavior associated with them). We compare the three major paradigms in simulation modeling: System Dynamics, Discrete Event and Agent Based Modeling with respect to how they approach such systems. We show in detail how an Agent Based model can be built from an existing System Dynamics or a Discrete Event model and then show how easily it can be further enhanced to capture much more complicated behavior, dependencies and interactions thus providing for deeper insight in the system being modeled.
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A Methodological Framework for Business-Oriented Modeling of IT Infrastructure Ariel Landau, Segev Wasserkrug, Dagan Gilat, Natalia Razinkov, Aviad Sela, and Sarel Aiber. 2004 Winter Simulation Conference (WSC’04), December 5-8, Washington, D.C., USA

The creation of IT simulation models for uses such as capacity planning and optimization is becoming more and more widespread. Traditionally, the creation of such models required deep modeling and/or programming expertise, thus severely limiting their extensive use. Moreover, many modern intelligent tools now require simulation models in order to carry out their function. For these tools to be widely deployable, the derivation of simulation models must be made possible without requiring excessive technical knowledge.
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Heterogeneity and network structure in the dynamics of diffusion Hazhir Rahmandad , John Sterman. MANAGEMENT SCIENCE Vol. 5. No. 5. May 2008

When is it better to use agent-based (AB) models, and when should differential equation (DE) models be used? Whereas DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity across individuals and in the network of interactions among them. AB models relax aggregation assumptions, but entail computational and cognitive costs that may limit sensitivity analysis and model scope. Because resources are limited, the costs and benefits of such disaggregation should guide the choice of models for policy analysis. Using contagious disease as an example, we contrast the dynamics of a stochastic AB model with those of the analogous deterministic compartment DE model. We examine the impact of individual heterogeneity and different network topologies, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Obviously, deterministic models yield a single trajectory for each parameter set, while stochastic models yield a distribution of outcomes.
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