Facility management involves a variety of processes with a large amount of data for managing and maintaining facilities. Processing and prioritizing work orders constitute a big part of facility management, given the large number of work orders submitted daily. Current practices for prioritizing work orders are mainly user-driven and lack consistency in collecting, processing, and managing a large amount of data. Decision-making methods have been used to address challenges such as inconsistency. However, they have challenges, including variations between comparisons during the actual prioritization task as opposed to those outside of the maintenance context. Data-driven methods can help bridge the gap by extracting meaningful and valuable information and patterns to support future decision-makings. Through a review of the literature, interviews, and survey questionnaires, this research explored different industry practices in various facilities and identified challenges and gaps with existing practices. Challenges include inconsistency in data collection and prioritizing work orders, lack of data requirements, and coping strategies and biases. The collected data showed the list of criteria and their rankings for different facilities and demonstrated the possible impact of facility type, size, and years of experience on criteria selection and ranking. Based on the results, this research proposed a methodology to automate the process of prioritizing work orders using Neural Networks. The research analyzed the work order data obtained from an educational facility, explained data cleaning and preprocessing approaches, and provided insights. The data exploration and preprocessing revealed challenges such as submission of multiple work orders as one, missing data for certain criteria, long durations for work orders' execution, and lack of correlation between collected criteria and the schedule. Through hyperparameter tuning, the optimum neural network configuration was identified. The developed neural network predicts the schedule of new work orders based on the existing data. The outcome of this research can be used to develop requirements and guidelines for collecting and processing work order data, improve the accuracy of work order scheduling, and increase the efficiency of existing practices using data-driven approaches. / Doctor of Philosophy / Facility Management (FM) is a profession that integrates various disciplines to ensure the comfort and safety of the occupants, efficiency of the built environment, and functionality of the building while meeting the main objectives of the owners. It involves various functions, including space management, communication, contract management, inspection, etc. Among many of these FM functions, maintenance-related tasks occupy 79% of the facility managers' responsibilities and %60 of the building cost in its whole lifecycle (design, construction, and operation). Prioritizing and processing work orders constitute a big part of facility maintenance management and requires a large amount of information submitted with hundreds of orders that need to be prioritized and turned into actions on a daily basis. Although vast amounts of work orders are submitted daily, the process of prioritizing orders has been done manually or partially through management systems rendering the process very challenging. The existing practices are highly dependent on the extent of knowledge, experience, and judgment of responsible staff available, are impacted by human cognitive workload and coping strategies and are challenged by inconsistency in data collection and uncertainty in decision-making. Delays in processing work orders can lead to asset downtimes and failure impacting occupants' comfort, health, and safety while increasing the cost of operation. Additionally, based on the results of previous studies, the alternative comparison for prioritizing work orders varies and is more realistic when performed during the actual work order prioritization task as opposed to outside of the maintenance context. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have provided opportunities to benefit from the historical data collected and stored by the facilities. Since a large number of work orders are generated and stored by facilities, such methods can be used to address the challenges with existing practices to reduce errors, downtimes, and asset failures and improve the operation of the buildings by supporting automation within the systems. This dissertation first aims to explore the existing practices for processing and prioritizing work orders and identifying their gaps and challenges. Second, it investigates the implementation of Artificial Neural Networks (ANNs) to automate the prioritization of future work orders. The ANN is one type of machine learning model which reflects and mimics the behavior of the human brain to understand the relationship between a set of data allowing computer programs to solve complex problems. This research will improve the existing practices for processing work orders by allowing the automation of future work order prioritization. It also provides the basis for the development of data requirements to further support existing practices.

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'Profession' and 'professional' are shifting signifiers that have taken on a range of new meanings in the past two decades as professional occupations have been reshaped by moves to 'flexible' (deregulated and decentred) work processes and work practices. The role of modern professions was significant in terms of the democratic elements of the professionalising project. But how do moves away from the modern bureaucratically-structured professions, and a professional ideal based on the concept of universal service, impact on graduates currently entering professional employment domains in which new 'performativity-based' management regimes are replacing the older control structures? This study draws on a range of sociological literature to explore both the structural and discursive changes in the meaning of profession practice. The study also draws on a number of research projects, including materials from focus group interviews of final year undergraduate students, recruitment brochures, ABS (Australian Bureau of Statistics) statistical analyses and DEST (Australia: Department of Employment, Science and Training) graduate destination studies, and policy documents. These materials are used to argue that the employment destinations of those with professional qualifications and credentials are now more stratified and more diverse and no longer necessarily coupled with a lifelong career. In addtion, the new management regimes that accompany the move to more flexible work processes and work practices are changing how those in professional work locations construct their sense of themselves as professional practitioners. Changes in the nature of professional work, and in the structural and discursive location of professional workers, have implications for education and training institutions. These institutions not only prepare workers for these occupational domains but are the main conduits through which access to work in the restructured labour markets is mediated. The study concludes by drawing attention to the need for educational research to be anchored in a 'sociology of employment' that is able to provide a more critical account of the relationship between education and training and entry into high status/low status employment domains.

The main purpose of this research is to present a solution that enable to manage efficient and reliable way, all of the information in relation to control, tracking and the hospital equipment maintenance. So, was taken as an object of study of Engineering Department of the Central Hospital of the Air Force of Peru, which presents a lot of administrative deficiencies character in its internal processes of reception, record and closing of Work Orders as well as the preventive and corrective maintenance of the hospital equipment of the HCFAP.The contemplated solution comprises from analysis and design to the development of some use cases more significant of the application. / El presente trabajo de investigación tiene como propósito fundamental presentar una solución que permita administrar de forma eficiente y confiable toda la información respecto al control, seguimiento y mantenimiento del equipamiento hospitalario. Para ello se tomó como objeto de estudio al Departamento de Ingeniería del Hospital Central de la Fuerza Aérea del Perú, el cual presenta muchas deficiencias de carácter administrativo en sus procesos internos de recepción, registro y cierre de Órdenes de Trabajo así como el mantenimiento preventivo y correctivo de los equipos hospitalarios del HCFAP. La solución contemplada abarca desde el análisis y diseño hasta el desarrollo de algunos casos de uso más significativos de la aplicación.