This thesis proposes a multidirectional methodological framework for a comprehensive ergonomic analysis and modelling of workflow for multi-modal vascular image-guided procedures (IGPs). Two approaches are employed to analyse the workflow: Discrete Event Simulation (DES) and purpose-oriented physical models. In contrast to previous studies, the proposed methodology looks in detail the actions carried out within the intervention rooms and the clinical experience during the procedures with three main objectives: to provide a deeper understanding of vascular procedures, to predict the impact of protocol modifications and to offer a framework to develop new image-guided protocols for the alternative use of Magnetic Resonance (MR) imaging in comparison with X-Ray Digital Subtraction Angiography (DSA). The methodological framework includes an assessment of commercial simulation software packages to evaluate their fitness to the specific requirements of this research. The novel methodology is applied to several cases studies of common vascular IGPs. In addition, a case of MR – guided focused ultrasound intervention demonstrates how it is possible to extend the framework to study non-vascular IGPs. The multi-disciplinary methodological framework described opens a new way to understand IGPs that could be used in prospective applications such as medical education and medical devices regulations.

The high complexity existent in businesses has required managers to rely on accurate and up to date information. Over the years, many tools have been created to give support to decision makers, such as discrete event simulation and artificial neural networks. Both tools have been applied to improve business performance; however, most of the time they are used separately. This research aims to interpret artificial neural network models that are applied to the data generated by a simulation model and determine which inputs have the most impact on the output of a business. This would allow prioritization of the variables for maximized system performance. A connection weight approach will be used to interpret the artificial neural network models. The research methodology consisted of three main steps: 1) creation of an accurate simulation model, 2) application of artificial neural network models to the output data of the simulation model, and 3) interpretation of the artificial neural network models using the connection weight approach. In order to test this methodology, a study was performed in the raw material receiving process of a manufacturing facility aiming to determine which variables impact the most the total time a truck stays in the system waiting to unload its materials. Through the research it was possible to observe that artificial neural network models can be useful in making good prediction about the system they model. Moreover, through the connection weight approach, artificial neural network models were interpreted and helped determine the variables that have the greatest impact on the modeled system. As future research, it would be interesting to use this methodology with other data mining algorithms and understand which techniques have the greatest capabilities of determining the most meaningful variables of a model. It would also be relevant to use this methodology as a resource to not only prioritize, but optimize a simulation model.

Discrete-event simulation can be a useful tool in analyzing complex system dynamics in various industries. However, it is difficult for entry-level users of discrete-event simulation software to both collect the appropriate data to create a model and to actually generate the base-case simulation model. These difficulties decrease the usefulness of simulation software and limit its application in areas in which it could be potentially useful. This research proposes and evaluates a data collection and analysis methodology that would allow for the programmatic generation of simulation models using production tracking data. It uses data collected from a GPS device that follows products as they move through a system. The data is then analyzed by identifying accelerations in movement as the products travel and then using those accelerations to determine discrete events of the system. The data is also used to identify flow paths, pseudo-capacities, and to characterize the discrete events. Using the results of this analysis, it is possible to then generate a base-case discrete event simulation. The research finds that discrete event simulations can be programmatically generated within certain limitations. It was found that, within these limitations, the data collection and analysis method could be used to build and characterize a representative simulation model. A test scenario found that a model could be generated with 2.1% error on the average total throughput time of a product in the system, and less than 8% error on the average throughput time of a product through any particular process in the system. The research also found that the time to build a model under the proposed method is likely significantly less, as it took an experienced simulation modeler .4% of the time to build a simple model based off a real-world scenario programmatically than it did to build the model manually.

This dissertation investigated issues surrounding grain harvest and transportation logistics. A discrete event simulation model of grain transportation from the field to an on-farm storage facility was developed to evaluate how truck and driver resource constraints impact material flow efficiency, resource utilization, and system throughput. Harvest rate and in-field transportation were represented as a stochastic entity generation process, and service times associated with various material handling steps were represented by a combination of deterministic times and statistical distributions. The model was applied to data collected for three distinct harvest scenarios (18 total days). The observed number of deliveries was within ± 2 standard deviations of the simulation mean for 15 of the 18 input conditions examined, and on a daily basis, the median error between the simulated and observed deliveries was -4.1%. The model was expanded to simulate the whole harvest season and include temporary wet storage capacity and grain drying. Moisture content changes due to field dry down was modeled using weather data and grain equilibrium moisture content relationships and resulted in an RMSE of 0.73 pts. Dryer capacity and performance were accounted for by adjusting the specified dryer performance to the observed level of moisture removal and drying temperature. Dryer capacity was generally underpredicted, and large variations were found in the observed data. The expanded model matched the observed cumulative mass of grain delivered well and estimated the harvest would take one partial day longer than was observed. Usefulness of the model to evaluate both costs and system performance was demonstrated by conducting a sensitivity analysis and examining system changes for a hypothetical operation. A dry year and a slow drying crop had the largest impact on the system’s operating and drying costs (12.7% decrease and 10.8% increase, respectively). The impact of reducing the drying temperature to maintain quality in drying white corn had no impact on the combined drying and operating cost, but harvest took six days longer. The reduced drying capacity at lower temperatures resulted in more field drying which counteracted the reduced drying efficiency and increased field time. The sensitivity analysis demonstrated varied benefits of increased drying and transportation capacity based on how often these systems created a bottleneck in the operation. For some combinations of longer transportation times and higher harvest rates, increasing hauling and drying capacity could shorten the harvest window by a week or more at an increase in costs of less than $12 ha-1. An additional field study was conducted to examine corn harvest losses in Kentucky. Total losses for cooperator combines were found to be between 0.8%-2.4% of total yield (86 to 222 kg ha-1). On average, the combine head accounted for 66% of the measured losses, and the total losses were highly variable, with coefficients of variation ranging from 21.7% to 77.2%. Yield and harvest losses were monitored in a single field as the grain dried from 33.9% to 14.6%. There was no significant difference in the potential yield at any moisture level, and the observed yield and losses displayed little variation for moisture levels from 33.9% to 19.8%, with total losses less than 1% (82 to 130 kg dry matter ha-1). Large amounts of lodging occurred while the grain dried from 19.8% to 14.6%, which resulted in an 18.9% reduction in yield, and harvest losses in excess of 9%. Allowing the grain to field dry generally improved test weight and reduced mechanical damage, however, there was a trend of increased mold and other damage in prolonged field drying.

The research described in this thesis is aimed to improve our knowledge of barriers to the use of OR (Operations Research) techniques in the manufacturing sector. Numerous issues have been previously identified, but the work has generally been unsystematic, or focused on the OR profession. This research is empirical, systematic, and compares a group of OR professionals to a group of manufacturing managers. The technology selected for analysis was DES (Discrete event simulation, a computer based technique for modeling complex manufacturing processes), and the companies chosen, as the focus for data collection, were based in Australia. Two major implications for practice are identified in the thesis. These two perspectives should be seen to be complementary, rather than opposing. OR professionals should place their knowledge within the mindset used by manufacturing managers, rather than attempting to displace that mindset. / Doctor of Philosophy (PhD)

<p>In line with the company-wide CS09 project being carried out at Volvo CE Components, Cell 14 will have changes in terms of distribution of machines and parts routing to meet the lean manufacturing goals established.  These changes are of course dependant on future production volumes, as well as lot sizing and material handling considerations.</p><p>In this context, an important emphasis is given to the awareness of the performance measures that support decision making in these production development projects.  By using simulation as a confirmation tool, it is possible to re-assess these measures by testing the impact of changes in complex situations, in line with the lean manufacturing principles.</p><p>The aim of the project is to develop a discrete event simulation model following the methodology proposed by Banks et al (1999).  A model of Cell 14 will be built using the software Technomatix Plant Simulation ® which is used by the Company and the results from the simulation study will be analyzed.</p>

In line with the company-wide CS09 project being carried out at Volvo CE Components, Cell 14 will have changes in terms of distribution of machines and parts routing to meet the lean manufacturing goals established.  These changes are of course dependant on future production volumes, as well as lot sizing and material handling considerations. In this context, an important emphasis is given to the awareness of the performance measures that support decision making in these production development projects.  By using simulation as a confirmation tool, it is possible to re-assess these measures by testing the impact of changes in complex situations, in line with the lean manufacturing principles. The aim of the project is to develop a discrete event simulation model following the methodology proposed by Banks et al (1999).  A model of Cell 14 will be built using the software Technomatix Plant Simulation ® which is used by the Company and the results from the simulation study will be analyzed.

Increasing competition from traditional and emerging channels has placed new emphasis on rapid innovation and continuous differentiation in every aspect of supply chain, from earliest production stage to the final distribution steps. To bridge the gap between brilliant ideas and successful business initiatives, leading companies implement engineering simulation particularly in logistics and supply chain management (LSCM). Discrete event simulation (DES) and system dynamics (SD) are two modeling approaches widely used in this field. However there are not much done researches about the applications of these simulation approaches in supply chain context of Swedish Manufacturing Industries (SMI). This study explores the application of DES and SD in LSCM of SMI by looking at the nature and level of issues modeled. Journal papers and master theses that use these modeling approaches to study supply chains, published between 1990 and 2012 are reviewed. A total of 39 articles are analyzed to identify the frequency with which the two simulation approaches are used as modeling tools in LSCM of SMI. Our findings suggest that DES has been used more frequently to model supply chains in SMI. The results also show that not all the LSCM issues have been addressed evenly and generally tactical/operational issues have been modeled more frequently. The results of this study inform the existing literature about the use of DES and SD in LSCM of SMI.

The creation of large-scale simulation models is a difficult and time-consuming task. Yet simulation is one of the techniques most frequently used by practitioners in Operations Research and Industrial Engineering, as it is less limited by modeling assumptions than many analytical methods. The effective generation of simulation models is an important challenge. Due to the rapid increase in computing power, it is possible to simulate significantly larger systems than in the past. However, the verification and validation of these large-scale simulations is typically a very challenging task. This thesis introduces a simulation framework that can generate a large variety of manufacturing simulation models. These models have to be described with a simulation data specification. This specification is then used to generate a simulation model which is described as a Petri net. This approach reduces the effort of model verification. The proposed Petri net data structure has extensions for time and token priorities. Since it builds on existing theory for classical Petri nets, it is possible to make certain assertions about the behavior of the generated simulation model. The elements of the proposed framework and the simulation execution mechanism are described in detail. Measures of complexity for simulation models that are built with the framework are also developed. The applicability of the framework to real-world systems is demonstrated by means of a semiconductor manufacturing system simulation model.

The problem of overcrowding is one of the serious issues that almost every emergency department (ED) in Taiwan has to face on daily basis. ED crowding results in adverse medical outcomes, decline in quality of care, and lack of the ability to provide instant medical care. One of the viable (but not necessarily preferable) solutions to ED overcrowding is ambulance diversion (AD). That is, ambulances would bypass the ED¡¦s unable to provide emergency medical service, and send patients to another emergency department. In many medical systems, ambulance diversion is being seen as a standard operating procedure, whose effectiveness needs to be carefully studied before making a sound policy. In this research, an input-throughput-output simulation model is proposed for simulating ED operation. A computer simulation program is developed based on this model to evaluate various AD initiating criteria, patient-blocking rules, and AD intervals. The crowdedness index, the patient waiting time for service, and the percentage of adverse patients were assessed to determine the impact of various AD policies. By appropriate parameter settings, this simulation model can represent medical resource providers of different scales. The results we obtained may offer insights for making effective AD policies.