Data processing: structural design – modeling – simulation – and em – Modeling by mathematical expression
Reexamination Certificate
2000-10-19
2004-07-27
Paladini, Albert W. (Department: 2125)
Data processing: structural design, modeling, simulation, and em
Modeling by mathematical expression
C700S028000, C705S007380
Reexamination Certificate
active
06768973
ABSTRACT:
The present invention relates to a method for finding solutions to a problem dependent on a number of criteria.
U.S. Pat. No. 5,940,816 discloses a method of this type using a set of solution generators, wherein the set of solution generators provides candidate solutions which are filtered to produce at least one candidate solution to the problem. This method is used to find solutions for transportation planning problems. Such a method shows the advantage of providing an enhanced understanding of the trade-offs inherent in the selection of a solution from a number of possible solutions. This known method however does not take into account the uncertainty of the data used.
The invention aims to provide an improved method for finding solutions to a problem dependent on a number of criteria.
According to the invention a method is provided for finding solutions to a problem dependent on a number of criteria, comprising the steps of
(i) providing a model algorithm for each of the criteria, each model algorithm providing a prediction for a corresponding criteria when a candidate solution is inputted into the model algorithm and wherein at least one model algorithm provides a prediction with a prediction error bar for the corresponding criteria; and
(ii) selecting criteria to optimise a set of candidate solutions;
(iii) and providing an algorithm for optimisation of the set of candidate solutions in accordance with the selected criteria;
wherein a first set of candidate solutions is provided, wherein the optimisation algorithm generates one or more new candidate solutions, wherein all candidate solutions are inputted into the number of model algoritnms to obtain predictions and at least one prediction error bar, and wherein information of the set of candidate solutions obtained by said generation and/or previous optimisations and/or experiments is used to select candidate solutions from the set to obtain an optimised set of candidate solutions.
In this manner a method is obtained which is particularly suitable to solve real-life problems. The uncertainty in the data of candidate solutions is advantageously used in the optimisation algorithm to obtain the optimised set of candidate solutions. The error bar(s) can be used for example to guide the optimisation process to meet certain performance requirements. The method allows to examine candidate solutions to select candidate solutions for actual testing thereby reducing the efforts to do actual tests. In this manner a solution to a problem can be obtained at reduced cost and use of test materials and equipment, An advantageous application of the method is optimising manufacturing processes.
According to a preferred embodiment the method of the invention can include 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 solutions are inputted into the number of model algorithms to obtain predictions whereby at least one prediction includes a prediction error bar; and
(iii) the optimisation algorithm generates new candidate solutions; and
(iv) the new candidate formulations are used as input into the number of model algorithms in iteration step (ii); and
wherein predictions and prediction error bars of the set of candidate solutions are used to select candidate solutions from the set to obtain an optimised set of candidate solutions.
Preferably candidate solutions are selected comprising predictions with minimum prediction error bars. In an embodiment of the method of the invention the predictions and/or prediction error bar(s) of the optimised set of candidate solutions are used to determine a region of an experimental space for carrying out further experiments.
In a preferred embodiment of the invention the optimisation process can be used for formulation optimisation.
For the purpose of the invention the term formulation optimisation refers to the fact that for a formulation the type of ingredients, their relative levels and the conditions of preparing the final formulation are chosen such that an desired end formulation is obtained.
For example an optimisation with reference to the type of ingredient may provide assistance is determining which choice out of a number of alternatives can be used. For example choice of emulsifiers, surfactants, thickeners etc.
An optimisation with reference to the relative level of ingredients may for example start from a list of ingredients and aim to find an optimised combination of those. For example the optimisation process may provide an indication of ratios of surfactant materials or fat mixtures in products.
An optimisation with reference to the conditions for preparing the final formulation may for example take into account processing conditions suchs as temperature, mixing speed, maturing times and aim to find optimal combinations of these.
Very often a formulation optimisation process in accordance to the invention will take into account more than one of the above elements. Preferred optimisation processes in according to the invention involve the optimisation with reference to the relative level of ingredients as described above. This may then optionally be combined with optimisation wrt to the type of ingredients and/or with reference to the manufacturing conditions.
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Bornstein Alan
Paladini Albert W.
Unilever Home & Personal Care USA , division of Conopco, Inc.
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