5 research outputs found
Pareto optimal matchings in many-to-many markets with ties
We consider Pareto optimal matchings (POMs) in a many-to-many market of applicants
and courses where applicants have preferences, which may include ties, over
individual courses and lexicographic preferences over sets of courses. Since this is the
most general setting examined so far in the literature, our work unifies and generalizes
several known results. Specifically, we characterize POMs and introduce the Generalized
Serial Dictatorship Mechanism with Ties (GSDT) that effectively handles ties
via properties of network flows. We show that GSDT can generate all POMs using
different priority orderings over the applicants, but it satisfies truthfulness only for
certain such orderings. This shortcoming is not specific to our mechanism; we show
that any mechanism generating all POMs in our setting is prone to strategic manipulation.
This is in contrast to the one-to-one case (with or without ties), for which
truthful mechanisms generating all POMs do exist
Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring
Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models
Cyber-physical LPG debutanizer distillation columns: machine-learning-based soft sensors for product quality monitoring
Summarization: Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.Παρουσιάστηκε στο: Applied Science