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.
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.
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.
System Dynamics, has been useful for a variety of disciplines; however, it has limitations in showing a geographical representation of the models under study. This paper proposes a methodology based on layered vectors which allows the use a city’s census information to feed a Geographic Information System (GIS). The GIS objects implemented into System Dynamics and located at coordinates X.Y.Z become the entry parameters for the simulation.
In mechanized tunneling a significant loss of performance resulting from weak spots in the supply chain or unforeseen geological conditions is a frequent and costly problem. Furthermore, disturbances of critical machine components can have such impact on the production that unforeseen modifications become necessary. Due to the sequential character the malfunction of one element might evoke cascading-effects which may result in a complete standstill of the tunneling progress. Transparent evaluation of applicable tunnel boring machine designs is essential in order to improve the productivity, avoid unplanned interruptions and to estimate the project duration in general. In order to meet these defiances, this paper presents a multimethod simulation model to investigate the advancement rate of tunnel boring machines. Processrelated disturbances can be considered easily within the presented simulation model. Simulation experiments demonstrate the purposive functionality of the model and visualize the significant influence of technical failure on the overall project performance
Software development projects are difficult to manage due to the high uncertainty in their various phases. Simulation is one of the tools that has been used to help software project managers produce project plans. Research into software process simulation modeling (SPSM) shows the dominance of discrete-event simulation and system dynamics. This paper supports the use of ABS in SPSM. We propose a practical effort function to estimate developers’ behavior. The other contribution of this paper is to demonstrate how the ABS model can be developed, calibrated and validated using data readily available to many software development companies/departments. This paper focuses on the construction phase of a tailored Rational Unified Process used in a geographically distributed software development department at AVL. The results look promising but more work needs to be done to include ABS into one of the mainstream simulation paradigms in SPSM
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.