Academic articles

A large class of systems being developed has both continuous time and discrete time behavior. In fact, any system that interacts with physical world falls in that class. Chemical, Automotive, Military, Aerospace are areas most frequently mentioned in this respect. To model such systems successfully and to get accurate and reliable results from simulation experiments one needs an executable language naturally describing hybrid behavior, and a simulation engine capable of simulating discrete events interleaved with continuous time processes. Additional problems arise with simulating hybrid systems in a distributed environment.

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.

In the old days, the price for IT services was formed in a pretty standardized way. Network services had an explicit usage price per Kbit/ sec. The range of provided IT services have been growing very fast and have reached new dimensions of complexity. From infrastructure pricing to web-enabled application availability and performance nowadays the old rules for defining service pricing is not applicable any more. Today it is difficult or sometime even impossible to associate the provided service levels with the cost related to the processes of operation, maintenance and the capital cost behind it. The old measures of dollars per Kbit/sec cannot be the right measure any more.

By creating an integrated simulation environment that models the underlying structure of a pharmaceutical enterprise portfolio it becomes possible to identify the optimal longitudinal allocation of finite resources across the constellation of available investment opportunities. The implementation of a hybrid approach that integrates multiple modeling techniques and analytic disciplines allows for a comprehensive environment that captures the underlying dynamics that drive observed market behavior. The implementation of an object oriented model structure constrains the models complexity by supporting dynamic re-use of both structure and logic.

In recent US Census data widely reported in the press “Hispanics” have become the largest minority group in the US. Using simulation modeling technology we look at some of the structural forces that shape the characteristics of the Hispanic population. The model creates a simulated Hispanic population whose level of acculturation to the broader population of which it is a part dynamically varies according to individual choice. The modeling technique used draws on both System Dynamic and Agent based paradigms both supported by innovative AnyLogic software. The representative Hispanic population is disaggregated down to the individual level as individual agents. Each agent makes choices stochastically as modulated by its current state and the outside environment that it is in.

Mathematical modeling is a relatively new but fast developing area of HIV studies providing researchers with an additional dynamical dimension in epidemiological work that allows scientists to simulate the consequences of various intervention and prevention scenarios. We illustrate these concepts by presenting a model that describes Injecting Drug Users (IDU) networks, injecting behavior and HIV/HCV spread within the networks. This individual-based (also called agent-based) model is used to investigate the impact of the introduction of Integralcannula syringes (ICS) instead of commonly used Detachable Needle syringes (DNS). Laboratory experiments have shown that ICS retain approximately 1000 times less residual blood (<.001 ml vs. 1ml) following injection and rinsing than DNS thereby decreasing risk of HIV/NCV transmission by nearly 100 times after 2 rinses.

Mathematical modeling is a relatively new but fast developing area of substance use field providing researchers with additional dynamical dimension in epidemiological work and allowing scientists to simulate the consequences of various intervention and prevention scenarios. We illustrate these concepts by presenting two models. The first model describes Injecting Drug Users (IDU) networks, injecting behavior and HIV/HCV spread among the networks. The size, structure of the networks as well as frequency of injecting and HIV risks were obtained from published literature on urban IDU networks. This individual-based model was used to investigate the impact of introduction of Integral-cannula syringes (ICS) instead of commonly used Detachable Needle syringes (DNS).

This article discusses General Motors’ North American Enterprise Model, a system dynamics model of the entire North American automobile market. The Enterprise Model takes a broad look across the corporation and its marketplace, combining internal activities such as engineering, manufacturing and marketing with external factors such as competition for consumer purchases in the new and used vehicle marketplaces. Eight groups of manufacturers compete monthly for a decade across eighteen vehicle segments, making segment-by-segment decisions about price, volume and investment.

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.

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.