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Cho, A. D., Carrasco, R. A., & Ruz, G. A. (2022). Improving Prescriptive Maintenance by Incorporating Post-Prognostic Information Through Chance Constraints. IEEE Access, 10, 55924–55932.
Abstract: Maintenance is one of the critical areas in operations in which a careful balance between preventive costs and the effect of failures is required. Thanks to the increasing data availability, decision-makers can now use models to better estimate, evaluate, and achieve this balance. This work presents a maintenance scheduling model which considers prognostic information provided by a predictive system. In particular, we developed a prescriptive maintenance system based on run-to-failure signal segmentation and a Long Short Term Memory (LSTM) neural network. The LSTM network returns the prediction of the remaining useful life when a fault is present in a component. We incorporate such predictions and their inherent errors in a decision support system based on a stochastic optimization model, incorporating them via chance constraints. These constraints control the number of failed components and consider the physical distance between them to reduce sparsity and minimize the total maintenance cost. We show that this approach can compute solutions for relatively large instances in reasonable computational time through experimental results. Furthermore, the decision-maker can identify the correct operating point depending on the balance between costs and failure probability.
Keywords: Maintenance engineering; Costs; Predictive models; Logic gates: Licenses; Decision making; Upper bound; Prescriptive maintenance; chance constraints; remaining useful life; stochastic optimization; LSTM networks
Genco, F., & Gengo, G. (2021). Selection of energy matrix sources in Chile using a fuzzy logic decision approach. Energy Syst., 12(2), 411–429.
Abstract: Chile's 2050 energy policy ultimate goals are to produce a sustainable model of economic growth respectful of the environment where energy is produced efficiently and reliably. Renewable energy sources are considered the main drive for developing by 2050 at least 70% of the total energy in Chile. This study aims to provide a quantitative analysis for the selection of the most sustainable energy production methods using the compromise ranking method (VIKOR) that uses maximum group utility for the majority and a minimum of individual regret for the opponent. Since all evaluations are provided via intervals, the possible degree theory is used to compare them. Nine major criteria are critically used for this purpose and prioritized using analytical hierarchical process (AHP). Since Chile's energy production matrix still relies heavily on fossil fuels with major concerns of GHG emissions, all major potential energy sources in Chile are considered including ocean energy in addition to nuclear energy. This study shows that biomasses are the best compromise solution and that traditional and modern nuclear energy plants score consistently better than solar power. Large hydro power plants rank very high but in light of the social opposition present in the country, they might not be easy to build as hoped. Ocean power is far superior to geothermal energy and comparable to wind power and for this reason it should be considered together with nuclear power for the future Chilean energy matrix.
Keywords: Multi-criteria decision making (MCDM); Chile 2050 Energy Policy; VIKOR method; Nuclear power; Fuzzy logic; Small modular reactors