Method for optimizing formulations

• US 6,757,667 B1
• Filed: 10/19/2000
• Issued: 06/29/2004
• Est. Priority Date: 04/12/2000
• Status: Active Grant

• Current Assignee: Unilever Home & Personal Care Usa Division Of Conopco, Inc.
• Original Assignee: Unilever Home & Personal Care Usa Division Of Conopco, Inc.
• Inventors: Patel, Shail
• Primary Examiner(s): Starks, Jr., Wilbert L.
• Assistant Examiner(s): Booker, Kelvin
• Application Number:
  US09/691,786
• Time in Patent Office: 1,349 Days
• Field of Search: 706/13, 706/19, 706/12, 706/21, 706/25, 706/16
• US Class Current: 706/19
• 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

Abstract

A method is described for using a computer for optimizing formulations against a number of criteria. A model algorithm is provided for each of the criteria, each model algorithm providing a prediction for a corresponding criteria when a candidate formulation is inputted into the model algorithm. Criteria are selected to optimize a set of candidate formulations. An algorithm is provided for optimization of the set of candidate formulations in accordance with the selected criteria. A first set of candidate formulations is provided and the optimization algorithm generates one or more new candidate formulations. All candidate formulations are inputted into the number of model algorithms to obtain predictions, and information of the set of candidate formulations obtained by generation and/or previous optimizations and/or experiments is used to select candidate formulations from the set to obtain a Pareto optimal set of candidate formulations.

Claims

1. A computer implemented method for optimising formulations wherein the formulation optimisation involves one or more of (a) optimisation of the type of ingredients in the formulation, (b) optimisation of the relative levels of ingredients in the formulation and (c) optimisation of the manufacturing conditions for the formulation against a number of criteria, comprising the steps of:

(a) providing a model algorithm for each of the criteria, each model algorithm providing a prediction for a corresponding criteria when a candidate formulation is inputted into the model algorithm; and

(b) selecting criteria to optimise a set of candidate formulations; and

(c) providing an algorithm for optimisation of the set of candidate formulations in accordance with the selected criteria; and

(d) creating computer-based output of the optimal set of candidate formulations;

wherein a first set of one or more candidate formulations is provided and wherein the optimisation algorithm generates one or more new candidate formulations and wherein all candidate formulations are inputted into the number of model algorithms to obtain predictions, and wherein information of the set of candidate formulations obtained by said generation and/or previous optimisations and/or experiments is used to select candidate formulations from the set to obtain a Pareto optimal set of candidate formulations.

2. A computer implemented method according to claim 1 including a number of iteration steps in a loop as follows:

(i) a first set of one or more candidate formulations are used as starting point;

(ii) candidate formulations are inputted into the number of model algorithms to obtain predictions, and

(iii) the optimisation algorithm generates one or more new candidate formulations; and

(iv) the new candidate formulations are used as input into the number of model algorithms in iteration of step (ii), and wherein a Pareto optimal set of predictions are determined for selecting the candidate formulations.

3. A computer implemented method according to claim 1, wherein the optimisation algorithm generates new candidate formulations using information of the generated candidate formulations to obtain Pareto optimal candidate formulations, the information comprising formulation components, predictions and, if available, prediction error bars, the gradients and estimated gradients of predictions and/or prediction error bars, the constraints, and generally any other information available.

4. A computer implemented method according to claim 1, wherein the optimisation algorithm generates new candidate formulations in accordance with the landscape characteristic of the formulation landscape.

5. A computer implemented method according to claim 4, wherein the optimisation algorithm is a genetic algorithm and new formulations are generated according to a line cross-over search operator.

6. A computer implemented method according to claim 1, wherein at least one model algorithm provides a prediction with a prediction error bar for the corresponding criteria, wherein the optimisation algorithm determines Pareto optimal sets of predictions and prediction error bar(s) to select candidate formulations.

7. A computer implemented method according to claim 6, wherein candidate formulations are selected comprising predictions with minimised prediction error bars.

8. A computer implemented method according to claim 6, wherein a Bayesian neural network algorithm is used as a model algorithm providing a prediction and prediction error bar.

9. A computer implemented method according to claim 1, wherein constraints are defined regarding the candidate formulations and/or the predictions and/or the prediction error bar(s), wherein these constraints are used in the optimisation algorithm in selecting and/or generating the candidate formulations.

10. A computer implemented method according to claim 1, wherein the candidate formulations are displayed against selected sets of two or more of said number of criteria by way of computer based output.

11. A computer implemented method according to claim 10, wherein constraints on specific criteria can be introduced during displaying the candidate formulations against these specific criteria.

12. A computer implemented method according to claim 1, wherein constraints on the criteria can be introduced in an interactive manner to filter the optimised set of candidate formulations.

13. A computer implemented method according to claim 1, wherein the information of the Pareto optimal set of candidate formulations is used to determine a region of an experimental space for carrying out further experiments.

14. A computer implemented method according to claim 13, wherein the results of the further experiments are used to improve one or more of the model algorithms.

15. A computer implemented method according to claim 1, wherein the first set of candidate formulations is generated in a random manner or is seeded from formulations selected from the group of previous actual formulations, previous candidate formulations and optimised sets of candidate formulations.

16. A computer implemented method according to claim 15, wherein candidate formulations for the first set of formulations are obtained by a weighted optimisation algorithm.

17. A computer implemented method according to claim 1, wherein the method is used for optimising detergent products, food products, personal care products, fabric care products, household care products, or beverages.

18. A computer implemented method according to claim 1 wherein the formulation optimisation involves an optimisation of the relative levels of ingredients in the formulation optionally in combination with optimisation of the type of ingredients or the optimisation of the manufacturing conditions for the formulation.

19. A computer program device readable by a computer, comprising a computer program executable by the computer for effecting the computer to carry out a method comprising the steps of:

(a) providing a model algorithm for each of the criteria, each model algorithm providing a prediction for a corresponding criteria when a candidate formulation is inputted into the model algorithm; and

selecting criteria to optimise a set of candidate formulations; and

(c) providing an algorithm for optimisation of the set of candidate formulations in accordance with the selected criteria; and

(d) creating computer-based output of the optimal set of candidate formulations;

wherein a first set of one or more candidate formulations is provided and wherein the optimisation algorithm generates one or more new candidate formulations and wherein all candidate formulations are inputted into the number of model algorithms to obtain predictions, and wherein information of the set of candidate formulations obtained by said generation and/or previous optimisations and/or experiments is used to select candidate formulations from the set to obtain a Pareto optimal set of candidate formulations.

20. A computer program in a format downloadable by a computer, comprising a computer program executable by the computer to install the program in the computer for execution to effect the computer to carry out a method comprising the steps of:

(a) providing a model algorithm for each of the criteria, each model algorithm providing a prediction for a corresponding criteria when a candidate formulation is inputted into the model algorithm; and

(b) selecting criteria to optimise a set of candidate formulations; and

(c) providing an algorithm for optimisation of the set of candidate formulations in accordance with the selected criteria; and

(d) creating computer-based output of the optimal set of candidate formulations;

wherein a first set of one or more candidate formulations is provided and wherein the optimisation algorithm generates one or more new candidate formulations and wherein all candidate formulations are inputted into the number of model algorithms to obtain predictions, and wherein information of the set of candidate formulations obtained by said generation and/or previous optimisations and/or experiments is used to select candidate formulations from the set to obtain a Pareto optimal set of candidate formulations.