The demand of rail travel and transport of goods by train has increased significantly in Sweden during the last decades. Due to the high utilisation level, significant capacity shortages is nowadays common on numerous lines within the railway system causing substantial delays and low punctuality. The demand is foreseen to continue to increase, especially since we live in a time of climate change and travelling by train is an environmentally sustainable alternative. The government of Sweden has initiated an investigation of potentially building a high-speed railway connecting Stockholm with Malmö and Gothenburg. This would reduce the strain on existing railways, but it would also demand large investment costs and the subject has therefore been highly debated in politics and by media. The purpose of this study is to investigate potential future high-speed train stations between Jönköping and Malmö. The route of the track has not yet been decided but Växjö and Värnamo are the two primarily discussed cities for a station along the route. This study therefore focuses on studying the capacity and robustness of these two stations but also for a potential high-speed train station. By developing event-based models of the stations in SimEvents, which is a simulation tool in Matlab, the trains and their movements could be visualized and studied. The capacity of Växjö and Värnamo station were simulated with different track formation and diverse track dedication. The theoretical capacity as well as different forecasted timetables for the year of 2040 was simulated for the stations and the results showed that the station in Värnamo has greater potential in dealing with future demand without needing costly refurbishments. One infrastructural solution was created for the highspeed train station according to Trafikverket’s technical specifications. The maximum speed for the railway switch was set to be 130 km/h since it was calculated to be the lowest possible speed to maintain the specified headway. A switch that could handle higher speeds would result in greater capacity of the station but in addition greater investment costs. The results from simulating the high-speed station emphasised the importance of homogenous traffic. It was also seen that it is easier to remain high robustness when there is a small number of great delays than if the system is exposed to multiple small delays.

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For any manufacturing process, one of the key challenges after a solid foundation has been built is how improvements can be made. Management has to consider how possible changes will affect both the process as a whole in addition to every individual part before implementation. The groundwork for this is to have a clear overview of every part and the possibility to investigate effects of changes. This thesis thus aims to provide a clear overview of the complex painting process at Volvo GTO in Umeå and a template for investigating how differently implemented changes will affect the process. The means for doing this is to use statistics, modeling and discrete event simulation. Modeling shall provide an approximate recreation of reality and the subsequent analysis shall take into account similarities and differences to estimate the effects of changes. Recreation of real-world data and variability is based on bootstrap resampling for multiple independent weeks of observations. Results obtained from simulation are compared to observed data in order to validate the model and investigate discrepancies. Given the results of model validation, modifications are implemented and information obtained from model validation is used to evaluate the results of the modifications. Further, strengths and weaknesses of the thesis are presented and a recommendation of altering the stance on process improvements is provided to Volvo GTO.

This thesis details a higher-fidelity, scalable simulation tool and model for message response time and bus utilization rate analysis for the Controller Area Network (CAN) protocol stack. This tool achieves higher fidelity than existing commercial and academic simulation tools by including details of the stack implementation that are often neglected, such as receive and transmit hardware buffer availability and usage policy (i.e., which messages are able to be copied to which buffer resources), and the buffer polling or queueing policies. Key details of these features have been identified by a thorough examination of CAN stack behaviour, taking into account the physical considerations of commercial CAN implementations. Inclusion of these details in the simulation can produce better accuracy by exposing certain priority inversion scenarios. Scalability is achieved by using a transaction-based modelling approach and modelling transmissions at the protocol level rather than the physical/bit level. The tool requires minimal user interaction, and system level model generation is automated using an AUTOSAR XML system description file (ARXML format) to specify network topology and message information (transmitter, receiver(s), period, length, etc.), and an Excel spreadsheet file (XLS or XLSX format) to specify node hardware/software implementation details (buffer resource details, polling loop rates, main control loop rates, etc.) as inputs. / Thesis / Master of Applied Science (MASc)

This thesis presents a simulation model for a Control Area Network (CAN) software stack, the Dual FIFO CAN (DFC) stack, and a method for identifying and incorporating the details of the host environment (hardware setup, operating system, etc.) into the implementation of the simulation model in order to achieve a high level of fidelity. The method enable the simulation model to produce more realistic simulation results that are close to real-life experiments of the target system compared to existing commercial and academic simulation tools, which mostly ignore the system details The simulation model is implemented based on the specification documents of the DFC stack as well as knowledge gained from real-life experiments about the DFC stack and its host environment, a dual-core Electric Control Unit (ECU) hardware test bench that runs a Real-Time Operating System (RTOS). Like the actual DFC stack, the simulation model offers features such as dual non-preemptive FIFO transmit queues and TX buffers, and reserved slots in the queues for higher-priority messages. By using the method introduced in this research, the simulation model also offers options, once enabled and configured with proper parameters, for simulating a host environment that has effects on the behaviors of the modeled CAN stack. And these features are not fully available in existing commercial and academic simulation tools. The model provides internal calibration values of the DFC stack as configurable parameters to the user, making it easy to customize the simulation. Configurable calibration values includes the total number of slots in the transmit FIFO queues, number of reserved slots in the queues, transmit-rate thresholds that decide to which transmit queue a message is routed and whether a message is eligible to enter the reserved slots of the queues, and together they determine the queuing behaviors of the DFC stack. The options for simulating a host environment (an ECU on a CAN network in a modern vehicle, for instance) is capable of recreating the timing effects (delays, jitters or other effects due to the processing load, physical limitation and internal implementation) of the target host environment on the simulation results. Both deterministic (constant values, etc.) and/or statistical (probability distributions, etc.) models can be used to configure each single timing effect from the simulated host environment. The simulation model is also automated to transmit a set of customized transmit message (configurable message ID, DLC, period and internal transmission priority) and process simulation results according to the purpose of the simulation (statistical analysis, plots of data, etc). These features make it possible for the simulation model to be used not only to simulate various customized simulation scenarios, but also for different purposes in various stages of the development process, for instance, a pre-experiment simulation run before a test bench experiment to test the correctness of the calibrations and predict the possible outcomes of the experiment, or, simulations for confirmation purposes in order validate the test bench data after the test experiment. The model is compatible with typical modeling, simulation and development environments as it is implemented in MATLAB SimEvents environment, which works with third-party CAN development tools such as Vector CANoe. It is also designed to work with the high-fidelity model of the Vector CAN protocol stack from Whinton (2016). / Thesis / Master of Applied Science (MASc)

In the United States, the Energy Independence and Security Act of 2007 mandates the annual production of 136 billion liters of renewable fuel in the US by 2022 (US Congress, 2007). As the nation moves towards energy independence, it is critical to address the current challenges associated with large-scale biofuel production. The biomass logistics network considered consists of three core operations: farmgate operations, highway-hauling operations, and receiving facility operations. To date, decision-making has been limited in post-production management (harvesting, in-field hauling, and storage) in farmgate operations. In this thesis, we study the impacts in the logistics network resulting from the selection of one of four harvest scenarios. A simulation model was developed, which simulated the harvest and filling of a Satellite Storage Location (SSL), using conventional hay harvest equipment, specifically, a round baler. The model evaluated the impacts of four harvest scenarios (ranging from short, October-December, to extended, July-March), on baler equipment requirements, baler utilization, and the storage capacity requirements of round bales, across a harvest production region. The production region selected for this study encompassed a 32-km radius surrounding a hypothetical bio-crude plant in Gretna, VA, and considered 141 optimally selected SSLs. The production region was divided into 6 sub-regions (i.e. tours). The total production region consisted of 15,438 ha and 682 fields. The fields ranged in size from 6 to 156 ha. Of the four scenarios examined in the analysis, each displayed similar trends across the six tours. Variations in the baler requirements that were observed among the tours resulted from variability in field size distribution, field to baler allocations, and total production area. The available work hours were found to have a significant impact on the resource requirements to fulfill harvest operations and resource requirements were greatly reduced when harvest operations were extended throughout the 9-month harvest season. Beginning harvest in July and extending harvest through March resulted in reductions in round balers ranging from 50-63%, as compared to the short harvest scenario, on a sub-regional basis. On a regional basis, beginning harvest in July and extending harvest through March resulted in baler reductions up to 58.2%, as compared to the short harvest scenario. For a 9-month harvest, harvesting approximately 50% of total switchgrass harvest in July-September, as compared to harvesting approximately 50% in October-December, resulted in reductions in round balers ranging from 33.3- 43.5%. An extended (9-month) harvest resulted in the lowest annual baler requirements, and on average lower baler utilization rates. The reduced harvest scenarios, when compared to the extended harvest scenarios, resulted in a significant increase in the number of annual balers required for harvest operations. However, among the reduced harvest scenarios (i.e. Scenario 3 and 4), the number of annual balers required for harvest operations showed significantly less variation than between the extended harvest scenarios (i.e. Scenarios 1 and 2). As a result, an increased utilization of the balers in the system, short harvest scenarios resulted in the highest average baler utilization rates. Storage capacity requirements were however found to be greater for short harvest scenarios. For the reduced harvest scenario, employing an October-December harvest window, approximately 50% of harvest was completed by the end of October, and 100% of total harvest was completed by the third month of harvest (i.e. December). / Master of Science