TECHNICAL FIELD

The present disclosure generally relates to automated conversions of computational frameworks to different types of computational frameworks for automated planning. More specifically, the present disclosure generally relates to converting discrete computational frameworks to continuous computational frameworks for automated planning. Even more specifically, the present disclosure generally relates to converting discrete computational frameworks to binary computational frameworks, and further to continuous computational frameworks for automated planning. Further, the present disclosure generally relates to a system and method for converting discrete computational frameworks to continuous computational frameworks for automated planning.

BACKGROUND

Automated planning is widely utilized in many different scenarios. For example, automated planning may be used in determining an optimal plan and design of a transmission network. Various computational frameworks may be used, which may provide varying levels of accuracy, and which may generate results using varying levels of computational resources, which is a common technical problem associated with computing. For example, a computational framework which utilizes integers (i.e., instead of real numbers) may generate results using a significant amount of rounding. A computational framework with integral solution space may use pre-defined approximation techniques to converge to an acceptable solution. As another example, a computational framework that supports a particular internal representation (with corresponding arithmetic routines) for real numbers may provide different results from a different computational framework that supports a different internal representation (with different corresponding arithmetic routines). This differs from human calculation, as a human is not concerned with the same constraints as for computer calculations (e.g., predetermined finite number of bits for storing values, internal routines for performing arithmetic on internal numeric representations, significant loss of accuracy with bit loss (e.g., bit cutoffs due to the internal numeric representations and corresponding internal routines) in iterative computation, etc.). As an example, a result of 3.4 may be rounded to a value of only 3. As computations progress (e.g., addition, multiplication, division of multiple values, or combinations thereof), such loss of accuracy may be significant, leading to results that may be unacceptably inaccurate. For example, if computation of a trajectory of a missile requires many calculations, and a particular sequence of computer operations results in multiple losses of accuracy, the missile may actually be ultimately controlled to hit “friendly” environments, which would likely be an unacceptable result of the computer computations. As another example, if a bank computes compound interest on loans using techniques that round iteratively, the loan customers may be charged unacceptably high interest amounts (while the bank may profit substantially more than agreed, over large numbers of loan customers), which may lead to adverse consequences for the bank.

Significant research has been performed since the advent of computing devices, to develop both hardware and software that may provide more accurate results, and it is common for different hardware and/or software to generate different results in solving common problems that may involve iterative manipulations of numeric values, with their respective internal representations, which may vary from one platform to another (including both hardware and software platforms).

For example, various automated planning systems may produce inaccurate, or only approximated results, which may adversely impact decision-making based on the results.

Further, there is a lack of techniques for determining discrete solutions to a model/framework with cannibalization existing between different objects in a portfolio, although there exist techniques that may provide solutions in a continuous space or may provide an approximate discrete solution—which may not always execute successfully.

An example relaxed solution followed by discretization generates a discrete solution to a non-linear optimization problem by solving it as a continuous problem, and then discretizing the solution by rounding it off to the nearest discrete solution in the solution dataset. However, it may be difficult to retain feasibility after rounding off. For example, a rule may be violated. Further, the technique may end with local optima in many cases.

An example simulation based technique may create all possible scenarios using discrete variable values and evaluating an objective function, filtering out infeasible scenarios in accordance with constraints, and selecting the optimal scenario from the remaining cases. However, scenario counts may increase exponentially over time, and thus generating all such scenarios may quickly become computationally infeasible.

There is a need in the art for a system and method that addresses the shortcomings discussed above.

SUMMARY

A system and method for determining a result that includes a solution in a discrete space for a non-linear optimization model is disclosed. The system and method solves the problems discussed above by automatically transforming a discrete optimization framework to a binary optimization framework, and then to a continuous framework with additional constraints, to achieve higher accuracy, in less time than conventional systems.

Further, such optimization of computer operations is resource effective, conserving processor time, memory usage, and overall improving processing efficiency. Additionally, such optimization may avoid becoming mired in local maxima, thereby improving performance to provide improved results.

Additionally, the system and method may execute successfully, and efficiently, to determine discrete solutions to a model/framework with cannibalization existing between different objects in a portfolio.

In one aspect, the disclosure provides a method for determining a result that includes a solution for a discrete non-linear optimization model for an optimization problem by: determining a non-linear relationship between a key performance indicator (KPI) to be optimized and at least one decision variable using econometric modeling, determining a discrete optimization framework for the optimization problem using a plurality of discrete values of decision variables of the optimization problem, determining a binary optimization framework from the determined discrete optimization framework based on automatically revising an optimization equation of the discrete optimization framework, automatically determining a continuous framework from the determined binary optimization framework based on imposing additional constraints and based on modifying the optimization problem, and generating the result. An action may be controlled based on the result.

In another aspect, the disclosure provides a non-transitory computer-readable medium storing instructions that are executable by one or more device processors to determine a result that includes a solution for a discrete non-linear optimization model for an optimization problem by: determining a non-linear relationship between a key performance indicator (KPI) to be optimized and at least one decision variable using econometric modeling, determining a discrete optimization framework for the optimization problem using a plurality of discrete values of decision variables of the optimization problem, determining a binary optimization framework from the determined discrete optimization framework based on automatically revising an optimization equation of the discrete optimization framework, automatically determining a continuous framework from the determined binary optimization framework based on imposing additional constraints and based on modifying the optimization problem, and generating the result. An action may be controlled based on the result.

In another aspect, the disclosure provides a system that includes a device processor and a non-transitory computer readable medium storing instructions that are executable by the device processor to determine a result that includes a solution for a discrete non-linear optimization model for an optimization problem by: determining a non-linear relationship between a key performance indicator (KPI) to be optimized and at least one decision variable using econometric modeling, determining a discrete optimization framework for the optimization problem using a plurality of discrete values of decision variables of the optimization problem, determining a binary optimization framework from the determined discrete optimization framework based on automatically revising an optimization equation of the discrete optimization framework, automatically determining a continuous framework from the determined binary optimization framework based on imposing additional constraints and based on modifying the optimization problem, and generating the result. An action may be controlled based on the result.

Other systems, methods, features, and advantages of the disclosure will be, or will become, apparent to one of ordinary skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features, and advantages be included within this description and this summary, be within the scope of the disclosure, and be protected by the following claims.

While various embodiments are described, the description is intended to be exemplary, rather than limiting, and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible that are within the scope of the embodiments. Although many possible combinations of features are shown in the accompanying figures and discussed in this detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or substituted for any other feature or element in any other embodiment unless specifically restricted.

This disclosure includes and contemplates combinations with features and elements known to the average artisan in the art. The embodiments, features, and elements that have been disclosed may also be combined with any conventional features or elements to form a distinct invention as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventions to form another distinct invention as defined by the claims. Therefore, it will be understood that any of the features shown and/or discussed in the present disclosure may be implemented singularly or in any suitable combination. Accordingly, the embodiments are not to be restricted except in light of the attached claims and their equivalents. Also, various modifications and changes may be made within the scope of the attached claims.

