Distributed object-oriented discrete event simulation

This paper presents criteria for an \u27ideal\u27 simulation language, compares four traditional simulation languages to this ideal and concludes that an object-oriented approach to simulation comes closer to the ideal than the traditional procedural approach. It also examines how the object-oriented approach can be very beneficial for distributing a simulation problem among several machines. A distributed object-oriented package is described and a manufacturing example written and explained using this package

Toward the development and implementation of object-oriented extensions for discrete-event simulation in a strongly-typed procedural language

The primary emphasis of this research is computer simulation. Computer simulations are used to model and analyze systems. To date, computer simulations have almost exclusively been written in procedural, strongly-typed languages such as FORTRAN or Pascal;Recent advancements in simulation research suggest an object-oriented approach to simulation languages may provide key benefits in computer simulation. The goal of this research is to combine the advantages of a simulation language written in a procedural, strongly-typed language with the benefits available through the object-oriented programming paradigm;This research presents a review of the methods of computer simulation. A significant portion of this research is devoted to a description of the development of the object-oriented simulation software in a strongly-typed, procedural language;The software developed in this research is capable of simulating systems with multiple servers and queues. Arrival and service distributions may be selected from the uniform, exponential, and normal family of distributions. Resource usage is not supported in the simulation program

Operations research and computers

operational research

Development of a standard framework for manufacturing simulators

Discrete event simulation is now a well established modelling and experimental technique for the analysis of manufacturing systems. Since it was first employed as a technique, much of the research and commercial developments in the field have been concerned with improving the considerable task of model specification in order to improve productivity and reduce the level of modelling and programming expertise required. The main areas of research have been the development of modelling structures to bring modularity in program development, incorporating such structures in simulation software systems which would alleviate some of the programming burden, and the use of automatic programming systems to develop interfaces that would raise the model specification to a higher level of abstraction. A more recent development in the field has been the advent of a new generation of software, often referred to as manufacturing simulators, which have incorporated extensive manufacturing system domain knowledge in the model specification interface. Many manufacturing simulators are now commercially available, but their development has not been based on any common standard. This is evident in the differences that exist between their interfaces, internal data representation methods and modelling capabilities. The lack of a standard makes it impossible to reuse any part of a model when a user finds it necessary to move from one simulator to another. In such cases, not only a new modelling language has to be learnt but also the complete model has to be developed again requiring considerable time and effort. The motivation for the research was the need for the development of a standard that is necessary to improve reusability of models and is the first step towards interchangability of such models. A standard framework for manufacturing simulators has been developed. It consists of a data model that is independent of any simulator, and a translation module for converting model specification data into the internal data representation of manufacturing simulators; the translators are application specific, but the methodology is common and illustrated for three popular simulators. The data model provides for a minimum common model data specification which is based on an extensive analysis of existing simulators. It uses dialogues for interface and the frame knowledge representation method for modular storage of data. The translation methodology uses production rules for data mapping

Chapter 6: Designing and Learning from Modeling and Simulations

Instruction message design with simulations is the use of technology to create virtual environments for cost-effective, safe, and authentic learning. This chapter presents a condensed history of simulation learning, an introduction to several approaches to design instructional simulations, and research based best practices that can be used to guide instructional designers. These best practices include the attention to fidelity or realism of the simulation, the removal of extraneous distractions from the design, and the inclusion of sight, sound, and haptic details that the learner will encounter in the real world. Augmented reality, or the blending of virtual and physical environments, as well as virtual reality, or the immersion of learners in synthetic environments, are also two related areas that will allow for innovative message design opportunities. Advances in technology have allowed for the use of simulations in a wider variety of instructional applications including K-12, higher education, and military training. This chapter describes several of these intriguing avenues

MULTIPLE DISCRETE-EVENT SIMULATION AND ANIMATION MODELS TO ASSIST MODERN MINING OPERATIONS

This research investigation was conducted to develop, execute, and analyze a collection of discrete-event system simulation and animation models for different modern mining operations and systems, including two open-pit gold mines, an aggregate mine (sand and gravel), an open-cast (strip) coal mine, and an underground mine evacuation operation. The mine simulation and animation models aimed to study and assess a wide range of practical unique and common "what if?" scenarios that the mine engineers and managers of the case studies posed in different aspects during the research. A comprehensive and detailed literature review was also performed to provide a summary of the published discrete-event system simulation projects and their applications in the mining and mineral industry. The simulation results of the investigation were effectively implemented to assist the engineers in maximizing the productivity of the mines, improving the operation processes, reducing the environmental impact of the haulage operations, and enhancing the equipment utilization in various case studies. In addition, due to the shortage of powerful and flexible computer simulation tools in designing and analyzing underground mining evacuation operations and rescue equipment with respect to the mine operating characteristics and layout, the discrete-event system simulation and animation technique was innovatively implemented for modeling these complex systems. GPSS/H® and PROOF Professional® were the simulation language and animation software used for this research work

Enhancing Understanding of Discrete Event Simulation Models Through Analysis

Simulation is used increasingly throughout research, development, and planning for many purposes. While model output is often the primary interest, insights gained through the simulation process can also be valuable. Insights can come from building and validating the model as well as analyzing its behaviors and output; however, much that could be informative may not be easily discernible through these existing traditional approaches, particularly as models continue to increase in complexity. This research extends current work in model analysis and program understanding to assist modelers in obtaining more insight into their models and the systems they represent. A primary technique for model understanding is analysis of model output; this research has developed new, complementary techniques. A significant point of this research is that the created tools do not necessitate that a modeler or model user be able to encode the model or have any coding expertise. Some of the information presented here could be produced by existing software development tools; however, most modelers today do not have the technical background to use such tools or to make use of the reports they can produce. Additionally, one of the significant details of this research is the focus on model aspects rather than simulation aspects: the tools developed here detail the model embedded in implementation code, not the code necessary for implementation. Source code tends to involve many issues unrelated to the model itself, such as data collection, animation, and tricks for efficient run-time behavior. Even when the modeler is an expert programmer, this other code often can obscure features of the model as implemented. Results indicate these tools and techniques, when applied to even modest simulation models, can reveal aspects of those models not readily apparent to the builders or users of the models. This work provides both model builders and model users with additional techniques that can give them improved understanding of their models