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- ItemAcesso aberto (Open Access)Sistema integrado de manutenção produtiva a partir de otimização para contextos industriais de pequeno e médio porte(Universidade Federal de São Paulo (UNIFESP), 2020-02-19) Picanco, Ailson Renan Santos [UNIFESP]; Salles Neto, Luiz Leduino De [UNIFESP]; Universidade Federal de São PauloIndustrial maintenance is the operational management of production process equipment that aims to ensure that machines operate as required, minimizing damage from unscheduled downtime. Observing the demands of the market and the objects of study in this area, a dilemma was found between investigations and research into increasingly complex problems and; the need for simplified solutions with low cost and easy implementation by organizations. From this gap, the objective of the work is to propose an integrated maintenance system based on the construction of optimization models under uncertainty and multicriteria decision analysis, oriented to small and medium industrial contexts. The research is based on the need to integrate optimization models for maintenance aimed at small and medium industries, as it understands that businesses of this dimension are relevant to the economy in terms of the contribution of GDP and in the generation of jobs, but that present demands for increased productivity and process development through management methods and models. In this sense, the system seeks to fill gaps in the choice of type of equipment maintenance, construction of the sequencing of activities, dimensioning of spare parts and component analysis based on reliability, with a focus on optimizing costs and the availability of productive resources. The implementation capacity, that is, technical and economic feasibility, of applying the models are fundamental boundary conditions, which guide the modeling process. Models were built to determine the global criticism for choosing the equipment allocated for preventive or corrective management, through a multicriteria approach combining cognitive map for extracting the criteria, TAW for the attribution of weights and clustering; and TOPSIS for the criticality ranking. A model for minimizing expected costs was built from the preventive maintenance schedule, as well as a cost minimization model for the corrective management of spare items. They were applied in two exploratory case studies developed in an intermodal sugar terminal and in a construction and machinery rental company, whose results point to a reduction in expected costs of 20 to 35%. Finally, as a facilitating measure for the application process, an implementation protocol was developed, punctuating the collection procedures, evaluation of variables and decisions, as well as analysis of risks and potential improvements.