Method for optimizing revenue or profit of a gambling enterprise

Abstract

A method optimizes revenue or profit for a gambling enterprise, such as a casino. The method employs a plurality of different gaming units, such as table games and electronic gaming devices, in the gambling enterprise. A plurality of counts is employed, one of the counts for each of the different gaming units. A plurality of decision functions is employed, with at least one of the decision functions for each of the different gaming units. Revenue or profit optimization is employed as an objective function. Optimal values for the counts for each of the different gaming units are determined from the decision functions and a plurality of constraints in order to optimize the objective function.

What is claimed is: 
     
         1 . A method for optimizing revenue or profit for a gambling enterprise, said method comprising:
 employing a plurality of different gaming units in said gambling enterprise;   employing a plurality of counts, one of said counts for each of said different gaming units;   employing a plurality of decision functions, at least one of said decision functions for each of said different gaming units;   employing revenue or profit optimization as an objective function;   determining with a processor optimal values for said counts for each of said different gaming units from said decision functions in order to optimize said objective function; and   adjusting said counts for each of said different gaming units to be said optimal values for said counts in said gambling enterprise.   
     
     
         2 . The method of  claim 1  further comprising:
 employing revenue optimization as said objective function; 
 employing at least one constraint for at least some of said different gaming units as one of said decision functions; 
 determining time series revenue data for said different gaming units and determining a plurality of demand functions, one of said demand functions for each of said different gaming units; 
 employing said demand functions as some of said decision functions; and 
 determining said optimal values from said at least one constraint and said demand functions. 
 
     
     
         3 . A method for optimizing revenue for a gambling enterprise, said method comprising:
 identifying a plurality of different classes of gaming units in said gambling enterprise;   employing a plurality of counts, one of said counts for each of said different classes;   employing at least one decision function for said different classes;   employing revenue optimization as an objective function;   determining time series revenue data for said different classes and determining a plurality of demand functions, one of said demand functions for each of said different classes;   determining with a processor optimal values for said counts for each of said different classes from said at least one decision function and said demand functions, in order to optimize said objective function and optimize revenue from said different classes of gaming units; and   adjusting said counts for each of said different classes to be said optimal values for said counts in said gambling enterprise.   
     
     
         4 . The method of  claim 3  further comprising:
 employing a predetermined time period of greater than one year for said time series revenue data and said demand functions. 
 
     
     
         5 . The method of  claim 3  further comprising:
 employing one of linear programming and non-linear programming to determine said optimal values for said counts. 
 
     
     
         6 . The method of  claim 3  further comprising:
 employing a plurality of constraints associated with said different classes of gaming units; 
 translating said constraints to a plurality of mathematical expressions; 
 employing said mathematical expressions as said at least one decision function; and 
 employing said demand functions and said mathematical expressions to determine said optimal values for said counts. 
 
     
     
         7 . The method of  claim 6  further comprising:
 employing one of linear programming and non-linear programming to determine said optimal values for said counts. 
 
     
     
         8 . The method of  claim 6  further comprising:
 determining whether said optimal values for said counts are reasonable values based upon said optimal values for said counts for each of said different classes resulting in a floor space of the different classes of the gaming units in said gambling enterprise being less than a predetermined value; 
 responsively adjusting said constraints; 
 translating said adjusted constraints to a plurality of corresponding mathematical expressions; and 
 re-determining optimal values for said counts for each of said different classes from said demand functions and said corresponding mathematical expressions, in order to optimize said objective function and optimize revenue from said different classes of gaming units. 
 
     
     
         9 . The method of  claim 6  further comprising:
 employing as one of said constraints a minimum count and a maximum count of a corresponding one of said different classes of gaming units. 
 
     
     
         10 . The method of  claim 3  further comprising:
 employing as said different classes of gaming units in said gambling enterprise a plurality of different classes of table games and a plurality of different classes of electronic gaming devices. 
 
     
     
         11 . The method of  claim 10  further comprising:
 differentiating said different classes of electronic gaming devices by denomination of wager; and 
 differentiating said different classes of table games by type of game. 
 
     
     
         12 . The method of  claim 3  further comprising:
 determining said time series revenue data from a user interface of a financial reporting system. 
 
     
     
         13 . The method of  claim 3  further comprising:
 employing regression analysis to convert each of said time series revenue data for said different classes to a mathematical expression for a corresponding one of said demand functions. 
 
     
     
         14 . The method of  claim 13  further comprising:
 for one of said time series revenue data for a corresponding one of said different classes, employing as said mathematical expression for a corresponding one of said demand functions a first type of mathematical expression; 
 selecting a different second type of mathematical expression; 
 employing regression analysis to convert said one of said time series revenue data for the corresponding one of said different classes to said different second type of mathematical expression for the corresponding one of said demand functions; and 
 employing one of said first type of mathematical expression and said second type of mathematical expression which provides a better fit of said one of said time series revenue data. 
 
     
     
         15 . The method of  claim 13  further comprising:
 analyzing said mathematical expression for each of said demand functions and selecting one of linear programming and non-linear programming to determine said optimal values for said counts for each of said different classes. 
 
     
     
         16 . The method of  claim 3  further comprising:
 employing a predetermined time period and a predetermined count of samples for said time series revenue data and said demand functions; and 
 including with said time series revenue data a revenue value and a count of said different classes of gaming units for each of said samples. 
 
     
     
         17 . The method of  claim 3  further comprising:
 employing at least one constraint associated with said different classes of gaming units; 
 translating said at least one constraint to at least one mathematical expression; 
 employing said at least one mathematical expression as said at least one decision function; 
 translating said demand functions to a plurality of polynomial equations; and 
 determining said optimal values from said at least one mathematical expression and said polynomial equations, in order to optimize said objective function. 
 
     
     
         18 . The method of  claim 3  further comprising:
 displaying said optimal values.