Shared autonomous vehicles present a significant opportunity to change the way that urban mobility is viewed by society. By providing a shared mobility platform at a cost lower than has previously been obtainable there are significant possibilites to enable a new era of mobility for consumers. This opportunity, however, comes with significant risks in the form of emissions and increased road usage. Understanding how the risks and benefits of shared autonomous vehicles can be balanced is crucial to be able to adequately prepare for their introduction. One of the primary ways to understand the interplay between the risks and benefits of autonomous vehicles is through the use of computer simulations. However, typically simulations must be defined for a specific area and provide results that are not applicable to a wide range of areas and situations. This work presents the development of a framework that can be used to simulate SAV behaviour at any given region of interest. This framework automates the process of generating a directed non-planar graph using data gathered from the OpenStreetMap project. It further provides tools to generate activity based trips that are statistically similar in time and density to provided data that reflects the trips in the simulation area. In the absence of this data, this work has identified the 2009 National Household Travel Survey as an acceptable surrogate for data specific to a region. The framework then provides methods by which the trip origins and destinations are mapped into the directed non-planar graph representation of the area of interest. This mapping is performed using real-world data including business locations and census data. Finally the framework is capable of simulating the activity of SAV in response to the defined trips given a variety of starting conditions and relocation strategies. In addition to the simulation framework this work presents a novel relocation strategy for unoccupied SAV based on the potential field methods that have been used in robotic navigation. This method provides a continously differentiable function that describes the unmet demand in the service area for a network of shared autonomous vehicles. The tunable parameters of the method are explored by using a design of experiments, and optimal values reflecting different scenarios are identified.The method is also evaluated in the context of both and over- and under-supply of vehicles for the given demand. As a result this method has been shown to provide substantial reductions in the wait time for a vehicle to service a trip with a minimal increase in the total distance that is traveled by all vehicles in the network.

Given recent advancements in technology and recognizing the evolution of smart manufacturing, the implementation of digital twins for factories and processes is becoming more common and more useful. Additionally, expansion in connectivity, growth in data storage, and the implementation of the Industrial Internet of Things (IIoT) allow for greater opportunities not only with digital twins but closed loop analytics. Discrete Event Simulation (DES) has been used to create digital twins and in some instances fitted with live connections to closely monitor factory operations. However, the benefits of a connected digital twin are not easily quantified. Therefore, a test bed demonstration factory was used, which implements smart technologies, to evaluate the effectiveness of a closed-loop digital twin in identifying and reacting to trends in production. This involves a digital twin of a factory process using DES. Although traditional DES is typically modeled using historical data, a DES system was developed which made use of live data with embedded machine learning to improve predictions. This model had live data updated directly to the DES model without user interaction, creating an adaptive and dynamic model. It was found that this DES with machine learning capabilities typically provided more accurate predictions of future performance and unforeseen near future problems when compared to the predictions of a traditional DES using only historic data

There is a significant rise in digital transformation, where all data types are easily available. Businesses seek to measure their processes to have better control over their operations as a result of the growth of digital information and technologies. By implementing a digital tool, industries are attempting to boost efficiency and productivity to survive in the fiercely competitive worldwide market. One of the most popular techniques of Industry 4.0 which enables a virtual depiction of a real system is simulation. Since improvements can be created and tested virtually before being implemented into the real system, simulation has become increasingly popular in the industrial sector. Particularly in Make to Order (MTO) and Engineer to Order (ETO) contexts, in which the manufacturing processes are complicated, non-standardized, and heavily reliant on manual labor, data capture has been an essential part that has gone unfocused, creating a long-lasting obstacle to digitalization. Companies must recognize the value of data and devise effective methods of data collecting as the economy shifts toward a more data-driven state. This study is based on an electrical transformer manufacturing company which is taking its initial steps towards using digitalization to better manage its manufacturing processes. However, the lack of data and the poor data quality resulting from manual data gathering methods are the main obstacles to this transformation. To look into the missing data, several organizational documents and production time documents were.

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