[pt] O setor de saúde, mais especificamente o ambiente hospitalar, pode ser classificado como um dos cenários de maior complexidade, no que se refere à cuidados com a saúde humana e a interface com outras aspectos indissociáveis para a execução de sua atividade-fim, como a arquitetura, os processos de gestão e monitoramento das condições ideais. Riscos de falhas e acidentes são inerentes a todo o processo do cuidado e existem alguns fatores que aumentam a probabilidade das falhas: a gravidade de doenças e estado do paciente; a tecnologia utilizada e seus mecanismos; o volume de atendimentos concorrendo com a atenção dos prestadores de cuidado; a velocidade com que as decisões precisam ser tomadas. Acidentes com ambiente, que promovam interrupções parciais ou definitivas e, em pior grau, danos ao paciente ou a população circulante, em um hospital, são lamentáveis e indesejáveis. No entanto, podem estar relacionados diretamente à assistência ao paciente, com os riscos com a terapêutica, com equipamentos e insumos ou ainda, com temas relacionados à infraestrutura (sistemas elétricos, sistemas de gases medicinais, sistemas de climatização, entre outros). O maior ou menor grau de vulnerabilidade de todo o sistema funcionante depende de como o tratamos, como o planejamos e o controlamos. Com metodologia de análise de conteúdo, as experiências de gestores da área de operações hospitalares formam a escolha do presente estudo, com representantes do segmento privado de grandes grupos de saúde do Brasil. Identificar os fatores intervenientes que afetam a gestão dos riscos, em especial, os estruturais, em hospitais, foi o objetivo do estudo. Destaque para a ótica do gestor de operações que faz parte da execução das atividades que permitem que o cotidiano aconteça, mas que não deve dissociar-se do planejamento de todo o mecanismo de funcionamento e estratégia de crescimento do negócio. Os registros provocaram nos próprios gestores, uma avidez por transformações a partir das oportunidades identificadas, com mais conexão com a gestão de riscos e suas derivações e aplicabilidades que afetam direta ou indiretamente toda a administração hospitalar, fomentando interesse por conhecimento e a expectativa de práticas mais seguras. / [en] The health sector, more specifically the hospital environment, can be classified as one of the most complex scenarios, with regard to human health care and the interface with other inseparable aspects for the execution of its core activity, such as architecture, management processes and monitoring of ideal conditions. Risks of failures and accidents are inherent to the entire care process and there are some factors that increase the probability of failures: the severity of illnesses and the patient s condition; the technology used and its mechanisms; the volume of care competing with the attention of care providers; the speed with which decisions need to be made. Environmental accidents, which cause partial or permanent interruptions and, to a worse extent, damage to the patient or the circulating population, in a hospital, are regrettable and undesirable. However, they may be related to patient care, risks associated with therapy, equipment, and supplies or even issues related to infrastructure (electrical systems, medical gas systems, air conditioning systems, among others). The greater or lesser degree of vulnerability of the entire functioning system depends on how we treat it, how we plan and control it. Using content analysis methodology, the experiences of managers in hospital operations were the choice for this study, with representatives from the private segment of large health groups in Brazil. Identifying the intervening factors that affect risk management, especially structural ones, in hospitals was the objective of the study. Highlighting the perspective of the operations manager who is part of the execution of activities that allow everyday life to happen, but which must not be dissociated from the planning of the entire operating mechanism and business growth strategy. The records provoked in the managers themselves, an avidity for transformations based on the opportunities identified, with more connection with risk management and its derivations and applicability that directly or indirectly affect the entire hospital administration, fostering interest in knowledge and the expectation of safers practices.

Background: Optimal scheduling is a common practice in various industries, facili-tating efficient workflow management. Accelerating the generation of schedules while maintaining their optimality could encourage broader adoption of this approach inindustry settings. Previous work has aimed to estimate the makespan for the JobShop Scheduling Problem, showing promising results. Objectives: Given the increasing demand for AI and Machine Learning (ML) solutions across industries, this research aims to explore the integration of ML techniquesinto optimal scheduling processes. Specifically, the goal is to develop a faster scheduling solution without compromising the optimality of the generated schedules. The proposed approach combines the effectiveness and speed of ML with the optimal results obtained from mathematical scheduling models. Methods: This thesis focuses on the Job Shop Scheduling (JSS) Problem, where a mathematical scheduler is tasked with minimizing the makespan of a set of jobs while following a predefined set of rules. An initial investigation is performed to establish if there is potential in providing the scheduler with its optimal makespan to decrease the scheduling time. To generalize the application of the concept, the study investigates the potential efficiency acceleration achieved by providing the scheduler with a Machine Learning estimated makespan. This involves training a Neural Network(NN) to estimate the optimal makespan of job sets, which is then utilized to speedup the scheduling process. Results: The preliminary investigation demonstrates that providing the scheduler with the optimal makespan results in an average speed-up of schedule generationby 24%. The results of the scheduling time with the NN estimated makespan is on the other hand not as well performing. Despite achieving a level of accuracy in estimating the makespan, the resulting speed-up in the scheduler’s performance falls short. For the scheduler to benefit from being provided an estimated makespan it is therefore theorized to require a close-to-perfect estimation of the makespan, which was not achieved with the trained NN model. The trained NN reached an average accuracy of 95.75%. Conclusions: The study concludes that while ML models can accurately estimate makespan, the observed speed-up in scheduling performance is not as significant as anticipated. The correlation between well-estimated makespan and speed-up appearsto be inconsistent, indicating potential limitations in the current approach. Further investigation into the search algorithm employed by the scheduling tool Gurobi mayprovide insights into optimizing the scheduling process more effectively. In summary, while the integration of ML techniques shows promise in accelerating scheduling processes, a higher accuracy of the ML model would be required. Additional researchis needed to refine the approach and potentially bring a faster optimal scheduling solution into the future. / Bakgrund: Optimal schemaläggning är en vanlig implemetation inom flera olika branscher och underlättar hantering och effektiviserar arbetsflöden. Att påskynda genereringen av scheman samtidigt som den optimala aspekten av schemaläggning inte går till spillo, skulle kunna främja en bredare användning av optimal schemaläggning för fler brancher. Tidigare undersökningar har gjorts för att estimera "makespan" för Job Shop problemet inom schemaläggning och har visat lovande resultat. Syfte: Med den ökande efterfrågan på AI- och maskininlärnings lösningar inom olika branscher syftar denna forskning till att utforska integrationen av ML-tekniker i den optimala schemaläggningsprocessen. Målet är att utveckla en snabbare schemaläggningslösning utan att kompromissa med det genererade schemats optimalitet. Det föreslagna tillvägagångssättet kombinerar ML’s effektivitet och hastighet med de optimala resultaten som den matematiska schemaläggningsmodellen erbjuder. Metod: Forskningen fokuserar på problemet med schemaläggning för jobbshoppen(JSSP), där en matematisk schemaläggare har i uppgift att minimera makespan fören uppsättning jobb med hänsyn till ett par fördefinierade regler. En initial under-sökning görs, vilket visar att det finns potential i att tillhandahålla schemaläggarendess optimala makespan för att minska schemaläggningstiden. För att generalisera tillämpningen undersöker studien den potentiella accelerationen som uppnås genomatt tillhandahålla schemaläggaren ett maskininlärt uppskattat makespan. Detta medför att träna ett neuralt nätverk för att uppskatta det optimala makespanet för en mängd jobbuppsättningar, som sedan används för att påskynda schemaläggningsprocessen. Resultat: Den preliminära undersökningen visar att schemaläggaren resulterar i igenomsnittlig hastighetsökning av schemagenereringen med cirka 24% när den får tillgång till det optimala makespanet för de givna jobben. Resultaten av schemaläggningstiden med det neurala nätverkets uppskattade makespan är dock lägre än förväntat. Trots att en viss noggrannhetsnivå uppnås vid estimeringen av makespanet, når den resulterande hastighetsökningen i schemaläggarens prestanda inte upp tillförväntningarna. För att schemaläggaren ska dra nytta av att tillhandahålla ett uppskattad makespan krävs en nära perfekt uppskattning av makespan, vilket inte uppnåddes med det tränade neurala nätverket. Slutsatser: Studien drar slutsatsen att även om ML-modeller kan uppskatta makespan någorlunda noggrant, är den observerade hastighetsökningen i schemaläggningen inte lika betydande som förväntat. Korrelationen mellan väl uppskattad makespan och hastighetsökning verkar vara inkonsekvent, vilket indikerar potentiella begränsningar i det nuvarande tillvägagångssättet. Vidare undersökning av sökalgoritmen som används av schemaläggningsverktyget Gurobi kan ge insikter för att optimera schemaläggningsprocessen mer effektivt. Sammanfattningsvis visar integrationen av ML-tekniker lovande resultat för att accelerera schemaläggningsprocesser, men en bättre estimering av makespan skulle krävas. Ytterligare forskning behövs för att förbättra tillvägagångssättet och potentiellt introducera en snabbare optimal schemaläggningslösning för framtiden.