In past years many industries have utilized simulation as a means for decision making. That wave has introduced simulation as a powerful optimization and development tool in the manufacturing industry. Input data collection is a significant and complex event in the process of simulation. The simulation professionals have grown to accept it is as a strenuous but necessary task. Due to the nature of this task, data collection problems are numerous and vary depending on the situation. These problems may involve time consumption, lack of data, lack of structure, etc. This study concentrates on the challenges of input data collection for Discrete Event Simulation in the manufacturing industry and focuses particularly on speed, efficiency, data completeness and data accuracy. It has been observed that many companies have recently utilized commercial databases to store production data. This study proposes that the key to faster and more efficient input data collection is to extract data directly from these sources in a flexible and efficient way. An approach is introduced here to creating a custom software tool for a manufacturing setting that allows input data to be collected and formatted quickly and accurately. The methodology for the development of such a custom tool and its implementation, Part Data Collection, are laid out in this research. The Part Data Collection application was developed to assist in the simulation endeavors of Lockheed Martin Missiles and Fire Control in Orlando, Florida. It was implemented and tested as an aid in a large simulation project, which included modeling a new factory. This implementation resulted in 93% reduction in labor time associated with data collection and significantly improved data accuracy.

Automated data acquisition has revolutionized the reliability of product design in recent years. A noteworthy example is the improvement in the design of aircrafts through field data. This research proposes a similar improvement in the reliability of process design of earthmoving operations through automated field data acquisition. The segment of earthmoving operations addressed in this research constitutes the truck-loader operation. Therefore, the applicability of this research extends to other industries involving truck-operation such as mining, agriculture and forest logging and is closely related to wheel-based earthmoving operations such as scrapers. The context of this research is defined by data collection needed to increase the validity of the results obtained by analysis tools such as simulation, performance measures and graphical representation of variance in an activity's performance, and the relation between operating conditions and the variance in an activity's performance. The automated cycle time data collection is facilitated by instrumented trucks and the collection of information on operating conditions is facilitated by image database and paper forms. The cycle time data and the information on operating conditions are linked together to form the experience database. This research developed methods to extract, quantify and understand the variation in each component of the earthmoving cycle namely, load, haul and return, and dump activities. For the load activity, the simultaneous variation in payload and load time is illustrated through the development of a PLT (PayLoad Time) Map. Among the operating conditions, material type, load area floor, space constraints and shift are investigated. A dynamic normalization process of determining the ratio of actual travel time to expected travel time is developed for the haul and return activities. The length of the haul road, sequence of gear downshifts and shift are investigated for their effect on the travel time. The discussion on the dump activity is presented in a qualitative form due to the lack of data. Each component is integrated within the framework of the experience database. The implementation aspects with respect to developing and using the experience database are also described in detail. The practical relevance of this study is highlighted using an example. / Ph. D.

This study addressed the complex issue of data collection in program evaluation. The researcher sought to understand the influences affecting the quality and utility of data in program evaluation. The data collection process was examined through a single case study of a bilingual education program located in California. Information for this study was obtained through open-ended interviews with project staff, classroom teachers, and external evaluators. Other sources of information included records, documents, a computer database, and electronic mail correspondence with program officers. The researcher used Non-numerical Unstructured Indexing Searching and Theory Building (NUD*IST) computer software to manipulate interview transcriptions, records and documents. Emerging from the data were key categories and themes that were presented in narrative form. The researcher found that data collection was grounded in the context in which it occurs and was therefore, highly dependent on program staff. Data collection requires willing, qualified staff with an understanding of technology, assessment, and evaluation methodology.

That data is being collected surely, does not come as a surprise to users. Although, the question that should be asked today goes more in the lines of, do we know to what extent? Do we need to be aware? Is it bad, is it good? Big data collection is a worm hole of endless discussion and general complexity but is at the same time essential to understand to some degree. This study tries to examine how Meta, as one of the biggest companies today, pushes material in their newsflow in relation to how audiences perceive data collection as a phenomenon. Through theoretical foundation and previous research it is argued how negligence and unawareness can present itself unhealthy in the dynamic of how data is collected. This is illustrated through qualitative methods using thematic analysis by interviews and qualitative analysis of text. Results came to show that there is an interesting point to make in the data dynamic where negligence and unawareness might be a fact.

Wireless sensor networks (WSNs) typically consist of a significant number of inexpensive sensor nodes, each of which is powered by a battery or another finite energy source that is difficult to replace because of the environment they are in or the cost of doing so. The applications of WSNs include military surveillance, disaster management, target tracking and monitoring environmental conditions. In order to increase the lifespan of WSNs, energy-efficient sensing and communication approaches for sensor nodes are essential. Recently, there has been an increase in interest in using unmanned aerial vehicles (UAVs) as portable data collectors for ground sensor nodes in WSN. Several approaches to solving effective communication between sensor nodes and the fusion center have been investigated in this thesis. Because processing, sensing range, transmission bandwidth, and energy consumption are always limited, it is beneficial not to use all the information provided at each sensor node in order to prolong its life span and reduce communication costs. In order to address this problem, first, efficient measurement quantization techniques are proposed using a single fusion center and multiple sensors. The dynamic bit distribution is done among all the sensors and within the measurement elements. The problem is then expanded to include multiple fusion centers, and a novel algorithm is proposed to associate sensors to fusion centers. The bandwidth distribution for targets which are being monitored by several sensors is addressed. Additionally, how to use the situation in which the sensors are in the coverage radius of multiple fusion centers in order to share the targets between them is discussed. Finally, performance bounded data collection algorithms are proposed where the necessary accuracy for each target is specified. In order to determine the minimum number of data collectors needed and their initial placement, an algorithm is proposed. When there are fewer fixed data collectors than there are regions to collect the data from, a coverage path planning method is developed. Since the optimal solution requires an enormous computational requirement and not realistic for real-time online implementation, approximate algorithms are proposed for multi-objective integer optimization problems. In order to assess each suggested algorithm's effectiveness, many simulated scenarios are used together with baselines and simple existing methods. / Thesis / Doctor of Philosophy (PhD)

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