Academic articles

Java Engine for UML Based Hybrid State Machines

One of the approaches to modeling hybrid systems is to assign algebraic-differential equations describing the continuous behavior to states of state machines that represent discrete logic. The resulting hybrid state machine is a powerful concept to specify complex interdependencies between discrete and continuous time behaviors. It, however, exposes the simulation engine to a number of problems, which we discuss. The hybrid state machine based approach presented in this paper is fully supported by UML-RT/Java tool TimeBroker developed at Experimental Object Technologies.

Modeling S-Class Car Seat Control with AnyLogic — Daimler-Chrysler Modeling Contest

In this paper we give an overview of the car seat model that was developed for Daimler-Chrysler modeling contest in year 2001 and was awarded the 1st prize. We outline the OO UML-RT based modeling approach that was used and the simulation tool AnyLogic that supports it, and discuss their main advantages with respect to automotive area.

Distributed simulation of hybrid systems with AnyLogic and HLA

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.

Decision Support Tool — Supply Chain

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.

A Modern Simulation Approach for Pharmaceutical Portfolio Management

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.

From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools

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.

A Methodological Framework for Business-Oriented Modeling of IT Infrastructure

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.