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Bill payment optimization using a genetic algorithm

  • US 7,756,787 B1
  • Filed: 08/11/2004
  • Issued: 07/13/2010
  • Est. Priority Date: 01/22/2003
  • Status: Active Grant
First Claim
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1. A computer-implemented method of determining an optimal bill payment plan corresponding to a plurality of payment obligations in accounts payable of a finance account while evaluating a plurality of objectives related to amounts of payments in the bill payment plan for a day, comprising a 24 hour period, the computer-implemented method comprising:

  • generating, by a computer processor, a genome population having a first set of vectors, each vector representing the bill payment plan, numerically defining for each payment obligation a payment amount; and

    modifying iteratively, by the computer processor, the genome population using a genetic algorithm until a vector of the first set of vectors forming the optimal bill payment plan is determined that maximizes a total amount of payment of the payment obligations on the day within an amount of available cash in the finance account for the day while simultaneously evaluating the plurality of objectives,wherein the genetic algorithm comprises determining a fitness for the vector of the first set of vectors, andwherein determining the fitness for the vector comprises;

    obtaining a plurality of objective values (OG) for the plurality of objectives, wherein the plurality of objective values represent a plurality of degrees of optimization of the plurality of objectives when the payment obligations are paid in accordance with the bill payment plan represented by the vector,normalizing and standardizing the plurality of objective values (OG) to obtain a plurality of normalized, standardized objective (NSO) values (Oi) for the plurality of objectives,obtaining a composite objective value (Ocomp) corresponding to the vector by the following equation;

    O comp =

    i = 1 n




    w i

    O i
    where  

    Ocomp is the composite objective value corresponding to the vector,  

    Oi is the NSO value corresponding to the plurality of objectives,  

    wi is a weight corresponding to the plurality of objectives, and  

    n is a number of objectives, and applying a fitness function to the composite objective value Ocomp to obtain a fitness score corresponding to the vector.

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