Requirements for Model Development Environments

This paper deals with the initial phase of our ongoing research project on the Definition of a Discrete Event Simulation MDE which started on 1 June 1983. The first phase of the rapid prototyping approach we are using in designing the MDE involves the requirements specification. A literature review revealed eleven current problems in modeling. To address these problems, a MDE was identified as composed of four layers: (1) hardware and operating system, (2) kernel MDE, (3) minimal MDE, and (4:) MDEs. Requirements were then perceived for each layer and are reported in this paper. The feasibility of the requirements have been assessed throughout our proto typing efforts. This paper has provided significant guidance to our research group in designing the MDE and its associated tools. We believe that the designers and implementers of other types of MDEs can benefit from the research described herein

Automatic programming of simulation models

The concepts of software engineering were used to improve the simulation modeling environment. Emphasis was placed on the application of an element of rapid prototyping, or automatic programming, to assist the modeler define the problem specification. Then, once the problem specification has been defined, an automatic code generator is used to write the simulation code. The following two domains were selected for evaluating the concepts of software engineering for discrete event simulation: manufacturing domain and a spacecraft countdown network sequence. The specific tasks were to: (1) define the software requirements for a graphical user interface to the Automatic Manufacturing Programming System (AMPS) system; (2) develop a graphical user interface for AMPS; and (3) compare the AMPS graphical interface with the AMPS interactive user interface

Objektive Qualitätsbewertung von Fahrdynamiksimulationen durch statistische Validierung

Simulationen gewinnen in der Entwicklung technischer Produkte stetig an Bedeutung. Insbesondere in der Automobilindustrie erfordern die Effizienz- und die Effektivitätssteigerung der Entwicklungsprozesse belastbare Simulationsergebnisse. Die Fahrdynamiksimulation nimmt hierbei eine wichtige Stellung ein. Bisherige Validierungsansätze führen zu einem ungenügenden Vertrauensnachweis, sodass häufig Zweifel an der Repräsentativität der Simulationsergebnisse in Bezug auf das reale Systemverhalten geäußert werden. Diese Forschungsarbeit stellt eine neue Methode für den objektiven Vergleich und die Bewertung der Übereinstimmungsgenauigkeit zweier Systemabbildungen vor. Ein wichtiges Anwendungsgebiet dieser Methode ist die Simulationsvalidierung, in der die Simulation und die praktische Messung die beiden Systemabbildungen sind. Beschrieben wird die Methode in dieser Arbeit für die Validierung von Fahrdynamiksimulationen, wobei als Referenz für die Simulationsergebnisse Messdaten aus dem Realversuch herangezogen werden. Dies entspricht einem typischen Anwendungsfall in der Automobilentwicklung und in der Fahrdynamikforschung. Die Arbeit gliedert sich in drei Teile. Im ersten Teil werden die Forschungsziele präzisiert. Auf Basis der Ist-Stand-Analyse und -Bewertung erfolgen eine Strukturierung des Validierungsprozesses und die Identifikation der Defizite, die den mangelhaften Vertrauensnachweis begründen. Der zweite Teil beschäftigt sich mit der anwendungsneutralen Methodenentwicklung, d.h. ohne Fokussierung eines spezifischen Anwendungsgebiets. Die aus der Ist-Stand-Bewertung folgenden Anforderungen zur Überwindung der Defizite werden in der Konzeptdefinition und in der Prozessverbesserung berücksichtigt. Die Effektivität der neuen Validierungsmethode resultiert aus der gleichzeitigen Betrachtung der Simulationsqualität für verschiedene Systemvarianten, die in dieser Arbeit Fahrzeugen mit unterschiedlichen Eigenschaften entsprechen. Hierdurch wird das Simulationsmodell in mehreren Arbeitspunkten getestet, sodass über eine statistische Auswertung für jede Ausgangsgröße der Simulationsanwendung zwei statistische Validitätsmaße bestimmt werden können, die die Prädiktionsqualität für absolute Kennwerte und für Kennwertdifferenzen angeben. Im dritten Teil erfolgt die Anwendbarkeitsanalyse in fahrdynamischen Validierungsstudien. Hier bewährt sich das neue Validierungskonzept und der überarbeitete Prozess ist ohne domänenspezifische Adaptionen durchführbar. Die Ergebnisse werden in einem Validitätsbewertungsbericht zusammengefasst, der neben der statistischen Validität auch Informationen über Versuchsunsicherheiten und zulässige Toleranzintervalle enthält. Die Bewertung der neuen Validierungsmethode führt zu dem Ergebnis, dass sie die Anforderungen erfüllt und durch die Steigerung des Vertrauens zu einem größeren Nutzen simulationsbasierter Untersuchungen in der Fahrdynamikforschung beiträgt

A framework for the provision of online discrete event simulation for operational decision support in complex manufacturing environments

The engineering body of knowledge contains an array of methodologies and techniques to address the effectiveness and efficiency of operational activities within a manufacturing environment. One such example is simulation modelling, a powerful analytical tool that can potentially be valuable in assisting decision makers, managers and engineers to gauge improvement opportunities and achieve process advancements. However, the cost of ownership for simulation models is not insignificant even for large multinationals, this stems from the requirements for specialist skills in simulation software, model development, data mining and statistical analysis. Simulation projects typically require a large investment to develop and usually are used-once-and-thrown-away. To reuse the model, it would require repeating a large portion of the development cycle. In order for simulation modelling to achieve wider recognition as a decision support tool there is a necessity to reduce the cost of model maintainability, promote reusability, increase flexibility and improve user friendliness. The research proposed framework intends to achieve four goals. i.) Improve and advance the deployment and maintenance requirements of simulation projects in comparison to traditional methods. ii.) Integrate automation into model deployment phase of a simulation projects. Thus, allowing unique user-specified simulation models to be generated by automatically extracting and manipulating data from factory databases. iii.) Enforce a strong documentation technique to achieve interoperability and re-traceability of project progress, therefore permitting programme code or even entire models to be reused and utilised in future projects. iv.) Advance user friendliness and acceptance towards simulation modelling. Reducing the expertise required to conduct simulation studies will improve the programming exercise image associated with typical simulation studies. This framework assists in developing customised simulation modules. These modules facilitate automated online rapid development of reconfigurable, flexible, self-maintaining simulation models, aiming to deliver tailored analysis to support real-time operational decision making

Semantic modelling for discrete event simulation.

Discrete event simulation modelling has been established as an important tool for management planning. This process has been aided by the availability of off-the-shelf simulation systems for microcomputers. Traditionally these have had text-based interfaces and very limited graphics. As the availability of powerful colour microcomputers have increased, graphical front-ends have been added. As clients have got used to consistent graphical interfaces (e.g. Apple Macintosh or Microsoft Windows), they have desired the same level of integration in their simulation support environments. Research in other fields has been utilised in improving simulation environments. These fields include relational databases, expert systems, formal languages and graphical environments. This thesis examines the use of artificial intelligence in the discrete event simulation field with the aim of examining some potential areas in which it might be possible to improve simulation environments. Existing simulation research in the artificial intelligence (AI) field is extended by investigating the graphical AI knowledge-base called semantic networks. This thesis demonstrates semantic modelling, a discrete event simulation modelling approach based on semantic networks, which attempts to give a consistent graphical interface throughout the life cycle of a simulation study. The semantic modelling approach also incorporates expert system and natural language research. A prototype system of this approach is described

A study on model design in the simulation of manufacturing systems.

Global competition in industry demands that organisations take steps to improve or even redesign their manufacturing systems in order to remain competitive. Such improvements invariably require considerable investment and risk. The use of computer simulation allows managers to understand the underlying dynamics of complex manufacturing systems in order to identify problem areas. The models can also be used to evaluate re-design strategies and options for improvement, thereby reducing the potential risk and increasing the likelihood of a positive return on investment However, developing a valid simulation model that represents the system to a sufficient scope and level of detail to allow confident decision making is a difficult task. The research explores the application of a novel methodology consisting of a questionnaire survey, case studies with expert model builders and action research with a steel manufacturing company. Using these research techniques, this study focuses on the crucial early phases of the simulation model development process. The research demonstrates that the combination and application of the research techniques has proved to be a powerful methodology to explore the dynamic interactions of the early stages of the simulation life cycle. The findings conclude that the simulation life cycle is highly iterative process where it is difficult to identify clear steps between the different stages of a simulation project. The model builders engage in a number of cyclic activities where there is significant interaction with the client stakeholders to ensure that the model is a valid representation of the problem. The increased use of Visual Interactive Simulation Software (VISS) has had a major impact on the life cycle by allowing dynamic models to be created at a very early stage which facilitates the interaction between model builder and client