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This research has highlighted the importance of the multiple roles in design and change organizations and the benefits to have quantitative tools and qualitative tools to support decision making. Hence the aims and objectives of this research are a model driven approach to support integrated decision making in MEs. The author has identified a gap in the lack of a systematic way to model MEs to facilitate integrated decision making. Initial Modelling Methodology (IMM) has been established based on Manufacturing System Integration (MSI) group at Loughborough University so that improvement of this methodology can be investigated to facilitate integrated decision making. Artwork is an industrial furniture manufacturer based in Loughborough and is used as the company case study of this research. IMM has been tested in exploratory research case 1 at a low level of production at Artwork. The experimentations of case 1 have been carried out to study the impact of product volume and variety on specific process section at low level. The result has shown that IMM lacks the ability to facilitate integrated decision making aspect and it can be developed to achieve aims and objectives. This leads to additional concepts of (1) modelling at different level of abstraction to realise the benefit of multiple levels of modelling, (2) deploying the improved views of W, P and R sub-systems to assist in exercising simulation modelling and (3) identifying possible users in planning with scope and focus of decision making. These three concepts were added into IMM and known as Extended Modelling Methodology (EMM). The EMM has been tested in exploratory research case 2 at a mid-level of production system at Artfrom with four types of experimentations: 1) balancing resources of production system, 2) demand change, 3) rework impact and 4) delay impact. The evaluation of results has shown a systematic way of the EMM to facilitate decision making individually and collectively. Therefore the overall research contributions are a new model driven approach to support conceptual design and change of manufacturing systems in aspect of integrated decision making. However, limitations of this research can be addressed as limited availability of data, range and detail of case studies and limited range of modelling techniques explored here. It follows that scopes of future works are utilization of EMM in other domains, consideration of other reference models, investigation of EMM in other company case studies and establishment of a comprehensive database applied in EMM and development of coherent simulation models. In addition, this thesis has also presented ongoing research on developing and testing EMM in another company case study in aircraft engine manufacturer.

We propose that for all situated agents, the process of task learning has many elements in common. A better understanding of these elements would be beneficial to both engineers attempting to design new agents for task learning and completion, and also to scientists seeking to better understand natural task learning. Therefore, this dissertation sets out our characterisation of agent-independent task learning, and explores its grounding in nature and utility in practise. We achieve this chiefly through the construction and demonstration of two novel task learning systems. Cross-Channel Observation and Imitation Learning (COIL; Wood and Bryson, 2007a,b) is our adaptation of Deb Roy’s Cross-Channel Early Lexical Learning System (CELL; Roy, 1999; Roy and Pentland, 2002) for agent-independent task learning by imitation. The General Task Learning Framework (GTLF) is built upon many of the principles learned through the development of COIL, and can additionally facilitate multi-modal, lifelong learning of complex skills and skill hierarchies. Both systems are validated through experiments conducted in the virtual reality-style game domain of Unreal Tournament (Digital Extremes, 1999). By applying agent-independent learning processes to virtual agents of this kind, we hope that researchers will be more inclined to consider them on a par with robots as tools for learning research.