公开(公告)号：US20220335308A1

公开(公告)日：2022-10-20

申请号：US17231064

申请日：2021-04-15

Applicant: International Business Machines Corporation

Inventor： Nianjun ZHOU ,  Viktoriia KUSHERBAEVA ,  Dharmashankar SUBRAMANIAN ,  Xiang MA ,  Jacqueline WILLIAMS ,  Nathaniel MILLS

IPC: G06N5/02 ,  G06F17/11

Abstract: A system and method for determining parameters for a system. Selectable questions with associated goal are presented. Operational goals are different from configurable parameters of the system. A question is selected and a goal indication is received. First values for each goal are determined by an optimization engine adjusting parameters of mathematical models for the system to improve a value of the goal associated with the selected question in a direction of the goal indication. Selectable questions and the first values are presented and selection of a second question and a second goal indication is received. An optimization engine determines second updated values by adjusting parameters of the mathematical model to improve a value of a goal associated with the second selected question in the direction of the second goal indication. Second updated values of the goals, and differences from the first updated values. are presented.

公开(公告)号：US20170199944A1

公开(公告)日：2017-07-13

申请号：US14991117

申请日：2016-01-08

Applicant: International Business Machines Corporation

Inventor： Maryam ASHOORI ,  Debarun BHATTACHARJYA ,  Jeffrey O. KEPHART ,  Dharmashankar SUBRAMANIAN

IPC: G06F17/30 ,  G06N5/04 ,  G06N7/00

Abstract: A system, computer program product, and method is described to provide a visualization tool which portrays the certain equivalent for one or more hypothetical (i.e. synthetic) or real probability distributions p(m), and optionally allows the user to manipulate that representation , resulting in associated changes to the underlying utility function u(m). In a first example, the risk preference visualization tool allows one to explore how the certain equivalent depends upon the probability distribution p(m), for a fixed utility function u(m). In a second example, the risk preference visualization tool allows one to explore how the certain equivalent depends upon the utility function u(m), assuming one or more fixed probability distributions pi(m), p2 (m), etc. In a third example, the decision maker can provide feedback through the visualization tool that would cause their utility function to be modified.

公开(公告)号：US20210012190A1

公开(公告)日：2021-01-14

申请号：US16507688

申请日：2019-07-10

Applicant: International Business Machines Corporation

Inventor： Pavankumar MURALI ,  Dharmashankar SUBRAMANIAN ,  Nianjun ZHOU ,  Xiang MA ,  Jacqueline WILLIAMS

IPC: G06N3/08 ,  G06N3/04 ,  G06F17/18

Abstract: An apparatus and method for optimizing a process, comprising: receiving live operational data associated with a plurality of sub-processes of a process; selecting a pre-trained regression model from a plurality of pre-trained regression models for each sub-process of the plurality of sub-processes; generating a system-wide optimization model comprising a multi-period mathematical program model, including: one or more decision variables; a plurality of constraints, wherein: a first constraint of the plurality of constraints comprises one of the pre-trained regression models, and a second constraint of the plurality of constraints comprises an operational constraint; and an objective function; generating, via the optimization model, an operating mode trajectory comprising a plurality of intermediate operating modes at a plurality of intermediate times during a planning interval; and displaying a set-point trajectory recommendation in a graphical user interface based on the operating mode trajectory.

公开(公告)号：US20230244752A1

公开(公告)日：2023-08-03

申请号：US17589092

申请日：2022-01-31

Applicant: International Business Machines Corporation

Inventor： Eliezer Segev WASSERKRUG ,  Orit DAVIDOVICH ,  Evgeny SHINDIN ,  Dharmashankar SUBRAMANIAN ,  Parikshit RAM

IPC: G06F17/17

CPC classification number: G06F17/17

Abstract: An example system includes a processor to receive historical data, a formal quality measure, a quality threshold, and a mathematical optimization model. At least part of the mathematical optimization model is generated from the historical data. The processor can measure a quality of the mathematical optimization model using the formal quality measure. The processor can then augment the mathematical optimization model such that the measured quality of the augmented mathematical optimization model exceeds the target quality threshold.