Knowledge-based modeling of discrete-event simulation systems

Modeling a simulation system requires a great deal of customization. At first sight no system seems to resemble exactly another system and every time a new model has to be designed the modeler has to start from scratch. The present simulation languages provide the modeler with powerful tools that greatly facilitate building models (modules for arrivals or servers, etc.). Yet, also with these tools the modeler constantly has the feeling that he is reinventing the wheel again and again. Maybe the model he is about to design already exists (maybe the modeler has designed it himself some time ago) or maybe a model already exists that sufficiently resembles the model to be designed. In this article an approach is discussed that deploys knowledge-based systems to help selecting a model from a database of existing models. Also, if the model is not present in the database, would it be possible to select a model that in some sense is close to the model that the modeler had in mind

Discrete event simulation tool for analysis of qualitative models of continuous processing systems

An artificial intelligence design and qualitative modeling tool is disclosed for creating computer models and simulating continuous activities, functions, and/or behavior using developed discrete event techniques. Conveniently, the tool is organized in four modules: library design module, model construction module, simulation module, and experimentation and analysis. The library design module supports the building of library knowledge including component classes and elements pertinent to a particular domain of continuous activities, functions, and behavior being modeled. The continuous behavior is defined discretely with respect to invocation statements, effect statements, and time delays. The functionality of the components is defined in terms of variable cluster instances, independent processes, and modes, further defined in terms of mode transition processes and mode dependent processes. Model construction utilizes the hierarchy of libraries and connects them with appropriate relations. The simulation executes a specialized initialization routine and executes events in a manner that includes selective inherency of characteristics through a time and event schema until the event queue in the simulator is emptied. The experimentation and analysis module supports analysis through the generation of appropriate log files and graphics developments and includes the ability of log file comparisons

Use of system dynamics and Easel for simulation of the software development process

Team software development is a complex and mostly unpredictable process and is characterized by inefficient use of staff and calendar resources. Given the magnitude of software development costs, a deeper understanding of the process may suggest ways to improve resource utilization. Simulation modeling is a useful approach to study the dynamics of complex systems. System dynamics characterizes systems as collections of interacting, non-linear feedback loops. The foundations of system dynamics were developed at MIT in the early 1950s. Since that time, system dynamics has been applied to a large number of complex system domains. In the early 1980s, the system dynamics simulation method was first used at MIT to develop a software development process model. A different approach to modeling complex systems is to use an actor, or property-based programming language. In a property-based model, the behaviors of individual entities are represented as concurrently executing threads, and discrete event clocks are used to simulate time. Easel is a new property-based programming language developed at the Software Engineering Institute housed at Carnegie Mellon University. Although determining the survivability of large-scale networks was the motivation to develop Easel, the SEI has conducted some initial work in applying Easel to the software development process domain. This thesis compared the use of system dynamics and Easel as tools to study the software development process. Both modeling approaches were used to test the validity of Brooks\u27s Law under different hiring strategies for small, medium, and large-scale projects. The models produced nearly identical results, and so provided a high level of confidence that the models were logically equivalent. The thesis concludes with a comparison of the two techniques based on background knowledge required, object representation, debugging difficulty, model maintainability, scalability, and timing control. A summary about the applicability of each technique is presented and recommendations for future work are offered

Discrete event simulation and virtual reality use in industry: new opportunities and future trends

This paper reviews the area of combined discrete event simulation (DES) and virtual reality (VR) use within industry. While establishing a state of the art for progress in this area, this paper makes the case for VR DES as the vehicle of choice for complex data analysis through interactive simulation models, highlighting both its advantages and current limitations. This paper reviews active research topics such as VR and DES real-time integration, communication protocols, system design considerations, model validation, and applications of VR and DES. While summarizing future research directions for this technology combination, the case is made for smart factory adoption of VR DES as a new platform for scenario testing and decision making. It is put that in order for VR DES to fully meet the visualization requirements of both Industry 4.0 and Industrial Internet visions of digital manufacturing, further research is required in the areas of lower latency image processing, DES delivery as a service, gesture recognition for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets

The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose