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This thesis develops econometric models of departure time and travel mode choice to evaluate dynamic transportation policies. Dynamic policies affect travel attributes differently throughout the day. Both departure time and mode choice are modelled with Random Utility Maximizing (RUM) Generalized Extreme Value (GEV) discrete choice models that capture systematic and random heterogeneity. Departure time is represented by a heteroskedastic generalized extreme value model (Het-GEV) with overlapping choice sets. Studying the Greater Toronto and Hamilton Area (GTHA), models are estimated using Revealed Preference (RP) household travel data from the 2006 Transportation Tomorrow Survey (TTS). Empirical models are used to evaluate dynamic transit and road pricing policies. Results indicate that the models are capable of capturing mode and time switching behaviour in response to peak pricing policies. To alleviate demand while maintaining transit mode share, a road charge and a moderate, flat, transit fare increase throughout the morning peak are recommended.

This thesis develops econometric models of departure time and travel mode choice to evaluate dynamic transportation policies. Dynamic policies affect travel attributes differently throughout the day. Both departure time and mode choice are modelled with Random Utility Maximizing (RUM) Generalized Extreme Value (GEV) discrete choice models that capture systematic and random heterogeneity. Departure time is represented by a heteroskedastic generalized extreme value model (Het-GEV) with overlapping choice sets. Studying the Greater Toronto and Hamilton Area (GTHA), models are estimated using Revealed Preference (RP) household travel data from the 2006 Transportation Tomorrow Survey (TTS). Empirical models are used to evaluate dynamic transit and road pricing policies. Results indicate that the models are capable of capturing mode and time switching behaviour in response to peak pricing policies. To alleviate demand while maintaining transit mode share, a road charge and a moderate, flat, transit fare increase throughout the morning peak are recommended.

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.

In naturally ventilated buildings, occupants play a key role in the performance and energy efficiency of the building operation, mainly through the opening and closing of windows. To include the effects of building occupants within building performance simulation, several useful models describing building occupants and their window opening/closing behaviour have been generated in the past 20 years. However, in these models, the occupants are classified based on the whole population or on sub-groups within a building, whilst the behavioural difference between individuals is commonly ignored. This research project addresses this latter issue by evaluating the importance of the modelling and prediction of occupants window behaviour individually, rather than putting them into a larger population group. The analysis is based on field-measured data collected from a case study building containing a number of single-occupied cellular offices. The study focuses on the final position of windows at the end of the working day. In the survey, 36 offices and their occupants were monitored, with respect to the occupants presence and window use behaviour, in three main periods of a year: summer, winter and transitional. From the behaviour analysis, several non-environmental factors, namely, season, floor level, gender and personal preference, are identified to have a statistically significant effect on the end-of-day window position in the building examined. Using these factors, occupants window behaviour is modelled by three different classification methods of building occupants, namely, whole population, sub-groups and personal preference. The preference-based model is found to perform much better predictive ability on window state when compared with those developed based on whole population and sub-groups. When used in a realistic building simulation problem, the preference-based prediction of window behaviour can reflect well the different energy performance among individual rooms, caused by different window use patterns. This cannot be demonstrated by the other two models. The findings from this research project will help both building designers and building managers to obtain a more accurate prediction of building performance and a better understanding of what is happening in actual buildings. Additionally, if the habits and behavioural preferences of occupants are well understood, this knowledge can be potentially used to increase the efficiency of building operation, by either relocating occupants within the building or by educating them to be more energy efficient.