International Business Machines Corporation

Pavankumar Murali

Nianjun Zhou

Lam Nguyen

Wesley M Gifford

Dung Tien Phan

Dzung Tien Phan

Sambit Sahu

Jayant R Kalagnanam

Ta-Hsin Li

Pietro Mazzoleni

Ming Li

Dharmashankar Subramanian

Jing Dai

Roman Vaculin

Younghun Kim

Ying Li

Ajay Ashok Deshpande

Milind R Naphade

Anshul Sheopuri

Patrick D Watson

Hongsheng Liu

Haoran Zhu

Binny Winston Samuel

Aspects of the invention include training an optimal interpretable decision tree for regression using mixed-integer linear programming techniques. A non-limiting example computer-implemented method includes receiving, using a processor, input data that includes time-series data. The method further includes training, using a binary mixed-integer linear program of the processor, an ODT for regression based on the input data. During the training process one or more outliers are filtered out by a linear loss model that minimizes training loss and outlier loss.

System-level control using tree-based regression with outlier removal

A computer implemented method for automatically generating an optimization model for site-wide plant optimization includes mapping a process flow diagram of a plant process to a graph comprising nodes and edges, wherein the nodes represent processes and the edges represent flows between processes. A behavior is learned for each node of the graph based at least on historic data of the plant process. One or more regression functions are modeled for each node to predict an output of each of the processes, wherein the one or more regression functions are modeled based on the learned behavior for each node.

Automated generation of optimization model for system-wide plant optimization

A computer-implemented method, computer program product, and computer system for automated model predictive control. The computer system trains multiple step look-ahead regression models, using historical states and historical actions for a to-be-optimized system, for each timestep of a past time horizon. Regression models may be either linear or nonlinear in order to capture process dynamics and nonlinearity. The computer system generates optimization constraints for each timestep of a future time horizon. The computer system generates optimization variables, based on the multiple step look-ahead regression models, for each timestep of the future time horizon. The computer system constructs a mixed integer linear programming based optimization model that includes an objective function, the optimization constraints, and the optimization variables. Nonlinear regression models are converted into piecewise linear approximation functions. The computer system solves the optimization model to produce actions for the to-be-optimized system, over the future time horizon, and recommend commitment-look-ahead actions.

Automated model predictive control using a regression-optimization framework for sequential decision making

Dynamic control of a production process of a manufacturing system is facilitated, where the control process includes receiving runtime input data for multiple input variables of the production process. The production process is represented, at least in part, by a physics-based expression, with at least one term of the physics-based expression being a function of two or more input variables of the production process. The control process includes determining coefficient and bias terms for a dynamic linear model connecting the multiple input variables and an output of the production process, where the terms are based, at least in part, on the input variables. The dynamic linear model and determined coefficient and bias terms are provided in an optimization model to generate a regression-optimization model which determines an optimized value of a control variable for the production process, which is used in facilitating control of the production process.

Regression-optimization control of production process with dynamic inputs

In example aspects of this disclosure, a method includes generating, by one or more computing devices, a parametric model that expresses condition states for each of a plurality of assets, and the probability of the assets transitioning between the condition states; generating, by the one or more computing devices, stochastic degradation predictions of a group of the assets, based on the condition states and the probability of transitioning between the condition states for at least some of the assets; and generating, by the one or more computing devices, a maintenance schedule based on: the stochastic degradation predictions of the group of the assets, costs of corrective maintenance for assets in a failed state, and costs of scheduled maintenance for the assets.

Intelligent dynamic condition-based infrastructure maintenance scheduling

Aspects of the invention include an optimal interpretable decision tree using integer linear programming techniques. A non-limiting example computer-implemented method includes receiving, using a processor, a plurality of data inputs from a process and selecting, using the processor, a data subset from the plurality of data inputs by solving linear programming to obtain a solution. The method builds and optimizes, using the processor, an optimal decision tree based on the data subset and alerts, using the processor, a user when a prediction of the optimal decision tree is greater than a threshold value.

Optimal interpretable decision trees using integer linear programming techniques

A system and a method of managing a manufacturing process includes receiving production data relating to the manufacturing process and determining an operational mode associated with the manufacturing process using historical, multivariate senor data. The method may further determine a recommended action to affect production based on the determined operational mode. The operational mode may be based on at least one of: a level of operation in a continuous flow process relating to a joint set of process variables, a representation of a joint dynamic of the set of process variables over a predefined length, and a joint configuration of an uptime/downtime of a plurality of units comprising a process flow.

Generating mode change alerts with automatic detection from sensor data

Aspects of the invention include implemented method includes selecting an optimization algorithm for the control system of a processing plant based on whether the control system is guided by a linear-based predictive model or a non-linear-based predictive model, in which a gradient is available. Calculating set-point variables using the optimization algorithm. Predicting an output based on the calculated set-point variables. Comparing an actual output at the processing plant to the predicted output. Suspending a physical process at the processing plant in response to the actual output being a threshold value apart from the predicted output.

Site-wide operations management optimization for manufacturing and processing control

A computer-implemented method for providing interpretable predictions from a machine learning model includes receiving a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y). An interpretability model is built corresponding to the predictive models, by assigning an interpretability to each prediction Ybased on the hierarchical structure. Assigning the interpretability includes decomposing X into a plurality of partitions Xusing the hierarchical structure, wherein X=UX, N being the number of partitions. Further, each partition is decomposed into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained. A score is computed for each partition as a function of the predicted scores of the sub-partitions, wherein the predicted scores represent interactions between the sub-partitions. Further, an interpretation of a prediction is outputted.

Predictive models having decomposable hierarchical layers configured to generate interpretable results

The disclosure relates to extraction of rationales for studied outcome. A method comprises: grouping features as expert to align with a set of operating practices; generating interpretable features using operating rules, combining with statistical dependence analysis to bin selected features to generate favorite practice actions; grouping features as expert that combine a subset of the interpretable features to align with a set of operating practices. The method can also comprise: using a neural network or deep learning component to quantify contribution of respective experts at a consumer level applying a generic additive approach; extracting feature importance at an individual consumer-level decomposed from expert level importance; evaluating alternative, what-if, scenarios through sensitivity analysis to identify favorite practice actions; consolidating a subset of the practice actions at client or stakeholder levels; and routing respective practice actions as a function of responsibility for the set of operating practices to stakeholders or consumers.

Facilitating extraction of individual customer level rationales utilizing deep learning neural networks coupled with interpretability-oriented feature engineering and post-processing

A computer-implemented method for controlling a production system includes mapping, by a controller, the production system as a directed acyclic graph. The production system can include multiple plants that are represented as nodes and relations between the plants represented by edges of the directed acyclic graph. The method further includes generating, by the controller, a regression model for each of the plants in the production system. The method further includes predicting, by the controller, an output of each plant based on sensor data associated from each plant. The method further includes adjusting, by the controller, one or more control variables for each plant based on a target output by using machine learning. The method further includes adjusting, by the controller, the one or more control variables for each plant to generate the target output.

Prediction optimization for system level production control

A method for targeting marketing for user conversion includes receiving a list of users. Data pertaining to the users is received. A conversion likelihood score representing an estimation of how likely the user would be to converted from a trial user to a paid user is determined for each user. A similarity score representing how similar the users of the pair are to one another is determined for each possible pair of users. A graph in which each node thereof represents each user and edges between the nodes have edge weights representing the determined similarity scores is constructed. Each node is associated with a value representing its conversion likelihood score. A marketing potential score is calculated for each user using both the node-associated-values and the edge weights of the graph. A set of target users having highest marketing potential scores is constructed.

Targeted marketing for user conversion

Selecting candidates from a set of candidates by receiving a set of input parameters about a single aspect of the candidates and applying multiple scoring methods to those input parameters to compute a candidate score via each scoring method. A subset of zero or more candidates is then selected from the set of candidates by applying one or more selection criteria to the set of scores. There is not necessarily a value for every input parameter of every candidate.

Robust selection of candidates

A system, method and program product for siting charging stations. A mobility detection module collects traffic data from vehicle sensors distributed in an area. A map matching module maps detected traffic in the area. A vehicle flow module temporally characterizes mapped traffic flow. An electric vehicle (EV) requirements (EVR) evaluator determines an optimal number of charging stations and respective locations for siting the charging stations.

Electric vehicle (EV) charging infrastructure with charging stations optimumally sited

Access is obtained to call data records (or other data) for a plurality of mobile telephony users. The call data records (or other data) include location-time sequences. For each of the mobile telephony users, corresponding ones of the location-time sequences are segmented into trips; a home-work-school classification model is used to determine a home location and a work-school location, based on the trips; and a trip purpose classification model is used to assign each of the trips a purpose label, based at least on comparing at least one of a start location and an end location for each one of the trips to at least one of the home location and the work-school location determined using the home-work-school classification model. The trips are aggregated into purpose based origin-destination categories based at least on the purpose labels, time ranges of the location-time sequences, and the start and end locations.

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Elmsford, NY, United States of America

Pavankumar Murali