Method for controlled optimization of enterprise planning models

• US 6,308,162 B1
• Filed: 05/21/1998
• Issued: 10/23/2001
• Est. Priority Date: 05/21/1997
• Status: Expired due to Term

• Current Assignee: SAP SE
• Original Assignee: Khimetrics, Inc. (SAP SE)
• Inventors: Ouimet, Kenneth J., Chaubal, Charu V.
• Primary Examiner(s): Hafiz, Tariq R.
• Assistant Examiner(s): Jeanty, Romain
• Application Number:
  US09/084,156
• Time in Patent Office: 1,251 Days
• Field of Search: 705/1, 705/7, 705/8, 705/9, 706/1, 706/38, 706/45, 706/13, 706/16, 706/19, 706/46
• US Class Current: 705/7.36
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals

Abstract

A computer-implemented method and system for controlled optimization of enterprise planning models is provided. This is accomplished by first defining an auxiliary objective function, which depends on the same variables as the model, or a subset thereof. An effective objective function is then constructed from the primary objective function by subtracting the auxiliary objective function multiplied by a weighting factor. The effective objective function is then optimized for a whole range of weighting values, yielding a table that describes how the primary objective function varies according to different values of the weighting factor. Optimization of the effective objective function with a given value of the weighting factor results in a particular value for the auxiliary objective. Thus, this computed table essentially provides a relationship between different realized values of the primary objective, the auxiliary objective, and all the variables of the enterprise planning model. The user is further provided with a way to specify a target value for the auxiliary objective to attain, and then use the table obtained previously to interpolate the value for the weighting factor that corresponds to the target value. This interpolated value for the weighting factor is then inserted into the effective objective function. This effective objective function is optimized, yielding the set of decisions which optimize the primary objective function while at the same time satisfying the constraint that auxiliary objective achieve a target value.

Claims

1. A computer-implemented method for controlling the optimization of an enterprise planning model while simultaneously satisfying at least one strategic constraint not taken into account in said enterprise planning model, said method comprising the steps of:

selecting a primary goal including a set of operational decisions, wherein said primary goal is represented by a primary objective function, said primary objective function being dependent upon a set of operational variables with each of said operational variables corresponds to one of said operational decisions.;

selecting at least one auxiliary goal, a subset of said set of operational decisions, wherein said auxiliary goal is represented by a constraint function that is dependent upon a subset of said set of operational variables;

forming an effective goal by combining said primary goal with said at least one auxiliary goal such that said auxiliary goal acts as a constraint on said primary goal, wherein said effective goal is represented by an effective objective function, which depends on said set of operational variables, wherein said effective objective function is formed by applying a weighting factor to each constrain function, and subtracting each weighted constraint function from said primary objective function; and

optimizing said effective goal with respect to each of said operational decisions through simulated annealing, thereby yielding a set of operational decisions that would maximize the primary goal, while simultaneously achieving said auxiliary goal, wherein said step of optimizing further includes optimizing said effective objective function with respect to each of said operational variables, thereby obtaining optimal values for each of said operational variables even when said set of operational variables includes at least one discrete variable.

2. A computer-implemented method for controlling the optimization of enterprise planning decisions through the application of a strategic constraint, said method comprising the steps of:

providing a data storage having enterprise data;

selccting a primary goal represented by a primary objective function, said primary objective function being dependent upon a set of operational variables, wherein each of said operational variables represents a single operational decision;

selecting an auxiliary goal represented by a constraint function that is dependent upon a subset of said set of operational variables, wherein said auxiliary goal is represented by a price image function given by;

$\frac{1}{N}<br><br>\underset{i=1}{\overset{N}{\sum <br><br>}}<br><br>\frac{P_i}{\stackrel{\_}{P_i}}\times <br><br>w_i$

where {overscore (P)}_i is an average price of an item i in a market of interest, w_i is a weighting function for item i and N is the total number of said items in the enterprise planning model;

selecting a targeted value for said auxiliary goal;

constructing an effective objective function, by combining said primary objective function with said constraint function multiplied by a weighting factor, said effcctive objective function depending from said set of operational variables;

selccting a weighting range for said auxiliary goal, wherein, for each weighting factor in said weighting range, said optimal values for said operational variables are utilized to determine the values of said primary goal and said auxiliary goal, and wherein the values of said primary goal, said auxiliary goal and said weighting factors are stored in a constraint overview table;

varying said weighting factor over said weighting range, wherein said steps of forming said effective function and optimizing said effective function are performed for each of said weighting factors in said weighting range;

interpolating data from said constraint overview table to estimate the value of the weighting factor that will yield that desired target value for said auxiliary goal;

repeating said steps of forming said effective goal and optimizing said effective goal for said estimated value of said weighting factor;

optimizing said effective objective function with respect to each of said operational variables, said enterprise data providing physical constraints on said optimization, thereby obtaining optimal values for each of said operational variables; and

utilizing said optimal values of said operational variables to determine a value for said primary goal and a value for said auxiliary goal, whereby said optimal values of said operational variables represent a set of operational decisions that should achieve said determined primary goal and said determined auxiliary strategic goal.