Methodology for input data modelling in the simulation of manufacturing systems.

Computer simulation is a well-established decision support tool in manufacturing industry. However, factors such as wrong conceptualisation, inefficient input data modelling, inadequate verification and validation, poorly planned experimentation and lengthy model documentation inhibit the rapid development and deployment of simulation models. A serious limitation among the above factors is inefficient data modelling. Typically, more than one third of project time is spent on identification, collection, validation and analysis of input data.This study investigated potential problems which influence inefficient data modelling. On the basis of a detailed analysis of data modelling problems, the study recommends a methodology to address many of these difficulties. The proposed methodology, discussed in this thesis, is called MMOD (Methodology for Modelling Of input Data). An activity module library and a reference data model, both developed using the IDEF family of constructs, are the core elements of the methodology. The methodology provides guidance on the best way of implementation and provide a tool kit to accelerate the data modelling exercise. It assists the modeller to generate a customised data model (entity model), according to the knowledge gained from the conceptualisation phase of the simulation project. The resulting customised data model can then be converted into a relational database which shows how the entities and relationships will be transformed into an actual database implementation. The application of the MMOD through simulation life cycle also enables the modeller to deal with important phases in the simulation project, such as system investigation, problems and objective definitions and the level of detail definitions. A sample production cell with different level of detail has been used to illustrate the use of the methodology. In addition, a number of useful methods of data collection and the benefits of using a MMOD approach to support these methods and data rationalisation which accelerates the data collection exercise are also covered. The aim of data rationalisation is to reduce the volume of input data needed by simulation models. This work develops two useful data rationalisation methods which accelerate the data collection exercise and reduce the model complexity. This work produced a novel approach to support input data modelling in simulation of manufacturing system. This method is particularly useful when the complex systems are modelled