In many areas of science, like computer science or electrical engineering, modeling languages have been established, however, this is not the case in the field of discrete processes (Weilkiens 2006). There are two reasons which motivate such a development.
This panel seeks to initiate a discussion within the production system simulation community about a fundamental change in the way we think about, teach, and implement production system simulation. Today, production system simulation, while based on formal simulation languages, is largely an artistic process.
The awareness of the greenhousegas effect and rising energy prices lead to initiatives to improve energy efficiency. These initiatives range from micro-generation, energy storage and efficient appliances to controllers with optimization objectives. Although these technologies are promising, their introduction may rise further questions. The implementation of such initiatives may have a severe impact on the electricity infrastructure. If several of these initiatives are introduced in a combined way, it is difficult to analyse their overall impact.
This paper presents a methodology to study the effect of different resolution strategies on the value of the investment in a project-specific dispute resolution ladder (DRL) using option/real option theories from financial engineering, process centric modeling, and system dynamics methodology.
Unlike fossil-fueled generation, solar energy resources are geographically distributed and highly intermittent, which makes their direct control difficult and requires storage units. The goal of this research is to develop a flexible capacity planning tool, which will allow us to obtain a most economical mixture of capacities from solar generation as well as storage while meeting reliability requirements against fluctuating demand and weather conditions. The tool is based on hybrid (system dynamics and agent-based models) simulation and meta-heuristic optimization.
Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, mitigating the threat of bio-warfare, and understanding the factors that may be responsible for the fall of ancient civilizations.
In the industrial engineering community, it’s a well-known adage that focusing on process can help achieve better results. In this second of a series of papers, we’ll focus on the process of simulation testing and outline how improving your testing process can lead to better results for your projects. We’ll consider model building as a software development exercise, and discuss how best practices from the broader software testing community can be applied for process improvement.
Current IT related optimization efforts focus on optimizing IT level metrics such as response times, availability, etc. What the business requires is that such IT optimization be carried out so as to optimize business objectives. Such optimization is not a one-time effort as there may be significant changes, (e.g. server failures, sudden increase in the number of users) that may render any existing policy sub-optimal. Such optimization can be led in AnyLogic.
IRS Office of Research Headquarters measures and models taxpayer burden, defined as expenditures of time and money by taxpayers to comply with the federal tax system. In this research activity, IRS created two microsimulation models using econometric techniques to enable the Service to produce annual estimates of taxpayer compliance burden for individual and small business populations. Additionally, a Discrete Event Simulation (DES) model was developed to represent taxpayer activities and IRS administration in postfiling processes.
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.