ADAPTIVE AND RELIABLE SYSTEM AND METHOD FOR OPERATIONS MANAGEMENT

• US 20030014379A1
• Filed: 07/01/1999
• Published: 01/16/2003
• Est. Priority Date: 07/01/1999
• Status: Abandoned Application

• Current Assignee: BIOS Group LP
• Original Assignee: BIOS Group LP
• Inventors: MACREADY, WILLIAM, MACDONALD, ROBERT, KAUFFMAN, STUART A., SAIAS, ISAAC, TOLLANDER, CARL, DARLEY, VINCE, COHN, JUDITH, LEVITAN, BENNETT, FEDERSPIEL, FRED
• Application Number:
  US09/345,441
• Publication Number:
  US 20030014379A1
• Time in Patent Office: Days
• US Class Current: 706/45
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

The present invention presents a comprehensive system and method for operations management which has the reliability and adaptability to handle failures and changes respectively within the economic environment. The present invention presents a framework of features which include technology graphs, landscape representations and automated markets to achieve the requisite reliability and adaptability.

Claims

1. A system for performing operations management in an environment of entities and resources comprising:

a plurality of resource objects characterizing the resources;

at least one selection operation for selecting one or more of said resource objects;

at least one transformation operation for combining said selected objects for forming at least one new resource in the environment; and

at least one graph operation for creating a graph representing the resources and said at least one transformation operation.

2. A system for performing operations management in an environment of entities and resources as in claim 1 wherein said plurality of objects comprise:

a plurality of offer objects characterizing offers of the resources; and

a plurality of request objects characterizing requests for the objects.

3. A system for performing operations management in an environment of entities and resources as in claim 2 wherein said plurality of request objects comprise a plurality of request attributes, r_j, j=1 . . . N, representing requested characteristics.

4. A system for performing operations management in an environment of entities and resources as in claim 3 wherein said plurality of offer objects comprise a plurality of offer attributes, o_k, k=1 . . . M, representing offered characteristics.

5. A system for performing operations management in an environment of entities and resources as in claim 4 wherein said selection operation comprises the steps of:

identifying matching ones of said request attributes, r_j, j=1 . . . N, with said offer attributes, o_k, k=1 . . . M to form a plurality of matching groups of said request objects and said offer objects;

evaluating said matching groups by computing how well said request attributes match said offer attributes; and

selecting at least one of said matching groups that are optimal with respect to said evaluation.

6. A system for performing operations management in an environment of entities and resources as in claim 5 wherein said request objects further comprise a plurality of attribute weights, w_j, j=1 . . . N, corresponding to said plurality of request attributes, r_j, j=1 . . . N, each of said weights, w, indicating an importance of said corresponding request attribute, r_j.

7. A system for performing operations management in an environment of entities and resources as in claim 6 wherein said matching groups are evaluated with respect to an evaluation function,

8. A system for performing operations management in an environment of entities and resources as in claim 5 wherein said offer objects further comprise a plurality of attribute values corresponding to said plurality of offer attributes, o_k, k=1 . . . M, each of said attribute values indicating how often said corresponding offer attribute had matched said plurality of request attributes, r_j, j=1 . . . N.

9. A system for performing operations management in an environment of entities and resources as in claim 8 wherein said offer objects further comprise a composite value defined as a sum of said plurality of attribute values.

10. A system for performing operations management in an environment of entities and resources as in claim 4 wherein said plurality of request attributes and said plurality of offer attributes are affordances.

11. A system for performing operations management in an environment of entities and resources as in claim 4 wherein said plurality of request attributes and said plurality of offer attributes comprise contract terms.

12. A system for performing operations management in an environment of entities and resources as in claim 4 wherein said plurality of offer attributes are selected from the group consisting of isa, hasa, and doesa.

13. A system for performing operations management in an environment of entities and resources as in claim 3 wherein said plurality of request attributes comprise needsa.

14. A system for performing operations management in an environment of entities and resources as in claim 1 further comprising at least one resource bus for receiving said offer objects and said request objects.

15. A system for performing operations management in an environment of entities and resources as in claim 14 further comprising at least one request broker for exchanging said offer objects and said request objects among said at least one resource bus.

16. A system for performing operations management in an environment of entities and resources as in claim 1 wherein said graph comprises a set of vertices, V, corresponding to the resources and a set of edges, E, corresponding to said at least one transformation operation.

17. A system for performing operations management in an environment of entities and resources as in claim 16 wherein said at least one graph operation comprises the steps of:

creating one or more vertices, v_o1, v_o2, corresponding to said one or more selected resource objects;

creating at least one vertex v_tl corresponding to said at least one new resource formed by said at least one transformation operation; and

creating at least one edge e corresponding to said at least one transformation operation wherein said at least one edge e has one or more origins corresponding to said one or more vertices, v_o1, V_o2, and has at least one terminus corresponding to said at least one vertex v_t1 corresponding to said at least one new resource.

18. A system for performing operations management in an environment of entities and resources as in claim 16 further comprising:

at least one graph analysis operation comprising the steps of;

identifying a plurality of paths P_i, i=1 . . . M through said graph; and

searching for at least one vertex v_p of said set of vertices, V, that is incident on two or more of said plurality of paths, P_l, i=1 . . . M to identify at least one corresponding polyfunctional resource.

19. A system for performing operations management in an environment of entities and resources as in claim 18 wherein said at least one graph analysis operation further comprises the step of accumulating the at least one corresponding polyfunctional resource.

20. A system for performing operations management in an environment of entities and resources as in claim 1 further comprising at least one model for representing a corresponding one of the entities.

21. A system for performing operations management in an environment of entities and resources as in claim 20 wherein said at least one model comprises:

a plurality of decision making objects to represent a corresponding plurality of decision making units within the entities; and

a plurality of connections among said plurality of decision making objects to represent a corresponding plurality of communication links among the decision making units.

22. A system for performing operations management in an environment of entities and resources as in claim 21 wherein said decision making objects comprise a plurality of attributes.

23. A system for performing operations management in an environment of entities and resources as in claim 22 wherein said plurality of attributes of said decision making objects comprise at least one line of sight indicator.

24. A system for performing operations management in an environment of entities and resources as in claim 22 wherein said plurality of attributes of said decision making objects comprise at least one authority indicator.

25. A system for performing operations management in an environment of entities and resources as in claim 21 further comprising:

at least one simulator for simulating said at least one model to determine the performance of the corresponding entity; and

at least one optimizer for determining values of said attributes of said structural objects and for determining said connections among said plurality of decision making objects to achieve an optimal performance of the corresponding entity.

27. A method for performing operations management in an environment of entities and resources as in claim 26 wherein said characterizing the resources step comprises the steps of:

characterizing offers of the resources with a plurality of offer objects; and

characterizing requests for the objects with a plurality of request objects.

28. A method for performing operations management in an environment of entities and resources as in claim 27 wherein said characterizing requests step comprises the step of representing requested characteristics with a plurality of request attributes, r_j, j=1 . . . N.

29. A method for performing operations management in an environment of entities and resources as in claim 28 wherein said characterizing offers step comprises the step of representing offered characteristics with a plurality of offer attributes, o_k, k=1 . . . M.

30. A method for performing operations management in an environment of entities and resources as in claim 29 wherein said selecting one or more of said resource objects step comprises the steps of:

identifying matching ones of said request attributes, r_j, j=1 . . . N, with said offer attributes, o_k, k=1 . . . M to form a plurality of matching groups of said request objects and said offer objects;

evaluating said matching groups by computing how well said request attributes match said offer attributes; and

selecting at least one of said matching groups that are optimal with respect to said evaluation.

31. A method for performing operations management in an environment of entities and resources as in claim 30 wherein said characterizing requests step further comprises the step of indicating an importance of said plurality of request attributes, r_j, j=1 . . . N with a corresponding plurality of attribute weights, w_j, j=1 . . . N.

32. A method for performing operations management in an environment of entities and resources as in claim 31 wherein said matching groups are evaluated with respect to an evaluation function,

33. A method for performing operations management in an environment of entities and resources as in claim 30 wherein said characterizing offers step further comprises the step of indicating how often each of said plurality of offer attributes match said plurality of request attributes, r_j, j=1 . . . N with a plurality of attribute values.

34. A method for performing operations management in an environment of entities and resources as in claim 33 wherein said characterizing offers step further comprises the step of defining a composite value for said offer objects as a sum of said plurality of attribute values.

35. A method for performing operations management in an environment of entities and resources as in claim 29 wherein said plurality of request attributes and said plurality of offer attributes are affordances.

36. A method for performing operations management in an environment of entities and resources as in claim 29 wherein said plurality of request attributes and said plurality of offer attributes comprise contract terms.

37. A method for performing operations management in an environment of entities and resources as in claim 29 wherein said plurality of offer attributes are selected from the group consisting of isa, hasa, and doesa.

38. A method for performing operations management in an environment of entities and resources as in claim 28 wherein said plurality of request attributes comprise needsa.

39. A method for performing operations management in an environment of entities and resources as in claim 26 further comprising the step of receiving said offer objects and said request objects with at least one resource bus.

40. A method for performing operations management in an environment of entities and resources as in claim 39 further comprising the step of exchanging said offer objects and said request objects among said at least one resource bus with at least one request broker.

41. A method for performing operations management in an environment of entities and resources as in claim 26 wherein said graph comprises a set of vertices, V, corresponding to the resources and a set of edges, E, corresponding to said at least one transformation operation.

42. A method for performing operations management in an environment of entities and resources as in claim 41 wherein said creating a graph step comprises the steps of:

creating one or more vertices, v_o1, V_o2, corresponding to said one or more selected resource objects;

creating at least one vertex v_t1 corresponding to said at least one new resource formed by said at least one transformation operation; and

creating at least one edge e corresponding to said at least one transformation operation wherein said at least one edge e has one or more origins corresponding to said one or more vertices,v_o1, v_o2, and has at least one terminus corresponding to said at least one vertex V_t1 corresponding to said at least one new resource.

43. A method for performing operations management in an environment of entities and resources as in claim 41 further comprising the steps of:

identifying a plurality of paths P₁, i=1 . . . M through said graph; and

searching for at least one vertex v_p of said set of vertices, V, that is incident on two or more of said plurality of paths, P_i, i=1 . . . M to identify at least one corresponding polyfunctional resource.

44. A method for performing operations management in an environment of entities and resources as in claim 43 further comprising the step of accumulating the at least one corresponding polyfunctional resource.

45. A method for performing operations management in an environment of entities and resources as in claim 26 further comprising the step of representing at least one of the entities with at least one corresponding model.

46. A method for performing operations management in an environment of entities and resources as in claim 36 wherein said representing at least one of the entities with at least one corresponding model step comprises the steps of:

representing a plurality of decision making units within the entities with a corresponding plurality of decision making objects; and

representing a corresponding plurality of communication links among the decision making units with a plurality of connections among said plurality of decision making objects.

47. A method for performing operations management in an environment of entities and resources as in claim 46 wherein said decision making objects comprise a plurality of attributes.

48. A method for performing operations management in an environment of entities and resources as in claim 47 wherein said plurality of attributes of said decision making objects comprise at least one line of sight indicator.

49. A method for performing operations management in an environment of entities and resources as in claim 47 wherein said plurality of attributes of said decision making objects comprise at least one authority indicator.

50. A method for performing operations management in an environment of entities and resources as in claim 46 further comprising the steps of:

simulating said at least one model to determine the performance of the corresponding entity; and

determining values of said attributes of said structural objects and determining said connections among said plurality of decision making objects to achieve an optimal performance of the corresponding entity.

51. A method for performing operations management in an environment of entities and resources as in claim 43 wherein said searching for at least one vertex v_p of said set of vertices, V, that is incident on two or more of said plurality of paths, P₁, i=1 . . . M to identify at least one corresponding polyfunctional resource step comprises the steps of:

selecting a first subset V′

of said set of vertices V;

determining at least two paths P₁, P₂ of said set of paths P₁, i=1 . . . m terminating at said subset of vertices V′

; and

performing an intersection of said at least two paths P₁, P₂ to identify at least one vertex v_p corresponding to the at least one polyfunctional resource.

26. A method for performing operations management in an environment of entities and resources comprising the steps of:

characterizing the resources with a plurality of resource objects;

selecting one or more of said resource objects;

combining said selected objects for forming at least one new resource in the environment with at least one transformation operation; and

creating a graph representing the resources and said at least one transformation operation.

52. Computer executable software code stored on a computer readable medium, the code for performing operations management in an environment of entities and resources, the code comprising:

code to characterize the resources with a plurality of resource objects;

code to select one or more of said resource objects;

code to combine said selected objects for forming at least one new resource in the environment with at least one transformation operation; and

code to create a graph representing the resources and said at least one transformation operation.

53. Computer executable software code stored on a computer readable medium, the code for performing operations management in an environment of entities and resources as in claim 52, the code further comprising:

code to characterize offers of the resources with a plurality of offer objects; and

code to characterize requests for the objects with a plurality of request objects.

54. Computer executable software code stored on a computer readable medium, the code for performing operations management in an environment of entities and resources as in claim 53, wherein the code to characterize requests further comprises code to represent requested characteristics with a plurality of request attributes, r_j, j=1 . . . N.

55. Computer executable software code stored on a computer readable medium, the code for performing operations management in an environment of entities and resources as in claim 54, wherein the code to characterize offers further comprises code to represent offered characteristics with a plurality of offer attributes, o_k, k=1 . . . M.

56. Computer executable software code stored on a computer readable medium, the code for performing operations management in an environment of entities and resources as in claim 52, wherein said graph comprises a set of vertices, V, corresponding to the resources and a set of edges, E, corresponding to said at least one transformation operation.

57. Computer executable software code stored on a computer readable medium, the code for performing operations management in an environment of entities and resources as in claim 56, wherein the code to create a graph further comprises:

code to create one or more vertices,v_o1, v_o2, corresponding to said one or more selected resource objects;

code to create at least one vertex v_t1 corresponding to said at least one new resource formed by said at least one transformation operation; and

code to create at least one edge e corresponding to said at least one transformation operation wherein said at least one edge e has one or more origins corresponding to said one or more vertices, v_o1, V,_o2, and has at least one terminus corresponding to said at least one vertex v_t1 corresponding to said at least one new resource.

58. Computer executable software code stored on a computer readable medium, the code for performing operations management in an environment of entities and resources as in claim 56, the code further comprising:

code to identify a plurality of paths P₁, i=1 . . . M through said graph; and

code to search for at least one vertex v_p of said set of vertices, V, that is incident on two or more of said plurality of paths, P₁, i=1 . . . M to identify at least one corresponding polyfunctional resource.

60. A programmed computer system for performing operations management in an environment of entities and resources comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 59, wherein the program code further includes:

code to characterize offers of the resources with a plurality of offer objects; and

code to characterize requests for the objects with a plurality of request objects.

61. A programmed computer system for performing operations management in an environment of entities and resources comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 60, wherein the code to characterize requests further includes code to represent requested characteristics with a plurality of request attributes, r_j, j=1 . . . N.

62. A programmed computer system for performing operations management in an environment of entities and resources comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 61, wherein the code to characterize offers further includes code to represent offered characteristics with a plurality of offer attributes, o_k, k=1 . . . M.

63. A programmed computer system for performing operations management in an environment of entities and resources comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 59 wherein said graph comprises a set of vertices, V, corresponding to the resources and a set of edges, E, corresponding to said at least one transformation operation.

64. A programmed computer system for performing operations management in an environment of entities and resources comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 63, wherein the code to create a graph further includes:

code to create one or more vertices,v_o1, v_o2, corresponding to said one or more selected resource objects;

code to create at least one vertex v_t1 corresponding to said at least one new resource formed by said at least one transformation operation; and

code to create at least one edge e corresponding to said at least one transformation operation wherein said at least one edge e has one or more origins corresponding to said one or more vertices,v_o1, v_o2, and has at least one terminus corresponding to said at least one vertex v_t1 corresponding to said at least one new resource.

65. A programmed computer system for performing operations management in an environment of entities and resources comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 64, wherein the code further includes:

code to identify a plurality of paths P₁, i=1 . . . M through said graph; and

code to search for at least one vertex v_p of said set of vertices, V, that is incident on two or more of said plurality of paths, P_i, i=1 . . . M to identify at least one corresponding polyfunctional resource.

59. A programmed computer system for performing operations management in an environment of entities and resources comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory, wherein the program code includes

code to characterize the resources with a plurality of resource objects;

code to select one or more of said resource objects;

code to combine said selected objects for forming at least one new resource in the environment with at least one transformation operation; and

code to create a graph representing the resources and said at least one transformation operation.

66. A method for exchanging a plurality of resources among a plurality of entities comprising the steps of:

defining a plurality of properties for the resources;

finding at least one match among said properties of the resources to identify a plurality of candidate exchanges; and

selecting at least one exchange from said plurality of candidate exchanges.

67. A method for exchanging a plurality of resources among a plurality of entities as in claim 66 wherein said selecting at least one exchange from said plurality of candidate exchanges step comprises the steps of:

defining a joint satisfaction as at least one function of said plurality of properties to measure a mutual satisfaction of said candidate exchanges; and

optimizing said joint satisfaction to identify one or more of said candidate exchanges having an optimal mutual satisfaction; and

selecting said one or more of said candidate exchanges having an optimal mutual satisfaction.

68. A method for exchanging a plurality of resources among a plurality of entities as in claim 67 wherein said optimizing said joint satisfaction step comprises the steps of:

decomposing the joint satisfaction to minimize

$\underset{1<br><br>j<br><br>N+1}{\sum <br><br>}<br><br>f_j<br><br>\left({\mathrm{\chi <br><br>}}_j\right)$

 

subject to at least one constraint,

$-{\mathrm{\chi <br><br>}}_{N+1}+\underset{1<br><br>j<br><br>N}{\sum <br><br>}<br><br>{\mathrm{\chi <br><br>}}_j<br><br>v_j=0$

wherein;

X_j=P_j and X_N+1=Σ

1≦

j≦

N^x_jv_j are new coordinates for jε

[1,N] and j=N+1 respectively;

f_j (X_j)=s^C_j(X_j|v_j) and f_N+1(X_N+1)=S^L(X_N+1) are new functions for jε

[1,N] and j=N+1 respectively;

s^C_j(X_j|vj) is a satisfaction of one of the entities participating in said candidate exchange and s^L(X_N+1) is a satisfaction of another of the entities participating in said candidate exchange.

69. A method for exchanging a plurality of resources among a plurality of entities as in claim 68 further comprising the step of:

introducing at least one Lagrange multiplier for said at least one constraint to form at least one Lagrangian,

$L<br><br>\left(x_1<br><br>\mathrm{\lambda <br><br>}\right)=\underset{1<br><br>j<br><br>N+1}{\sum <br><br>}<br><br>f_j<br><br>\left({\mathrm{\chi <br><br>}}_j\right)+\mathrm{\lambda <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>{a<br><br>\text{\hspace{1em}<br><br>}}^t<br><br>x\equiv <br><br>\underset{1<br><br>j<br><br>N+1}{\sum <br><br>}<br><br>L_i<br><br>\left({\mathrm{\chi <br><br>}}_j,\mathrm{\lambda <br><br>}\right)$

wherein

L_i(X_i,λ

)=f_l(X_i)+λ

a_iX_l and a_i=v_l for iε

[1,n]; and

and a_l+1=−

1.

70. A method for exchanging a plurality of resources among a plurality of entities as in claim 69 further comprising the step of minimizing said Lagrangian.

71. A method for exchanging a plurality of resources among a plurality of entities as in claim 70 wherein said step of minimizing said Lagrangian uses Lagrangian relaxation.

72. A method for exchanging a plurality of resources among a plurality of entities as in claim 71 further comprising the step of:

decomposing said Lagrangian into N 1-dimensional minimizations min_x L(x, λ

_t)=Σ

<sub>1≦

i≦

N</sub> min _XlL_i(X_l,λ

_t) to obtain a solution x_t=x(λ

_t).

73. A method for exchanging a plurality of resources among a plurality of entities as in claim 72 wherein said N 1-dimensional minimizations are performed in parallel.

74. A method for exchanging a plurality of resources among a plurality of entities as in claim 73 further comprising the step of determining said at least one Lagrangian multiplier using a dual function,

q(λ

)

within an expression,

75. A method for exchanging a plurality of resources among a plurality of entities as in claim 74 wherein said determining said at least one Lagrangian multiplier step comprises the step of maximizing said dual function,

q(λ

).

76. A method for exchanging a plurality of resources among a plurality of entities as in claim 75 wherein said maximizing said expression step uses steepest ascent.

77. A method for exchanging a plurality of resources among a plurality of entities as in claim 76 wherein said maximizing said expression step using steepest ascent comprises the step of computing the gradient of said dual function as:

78. A method for exchanging a plurality of resources among a plurality of entities as in claim 77 wherein said maximizing said expression step using steepest ascent comprises the step of updating said Lagrangian multiplier as:

λ

_t+1=λ

_t+α

a^tx(λ

).

wherein α

is a step size to determine at least one local peak for said Lagrangian multiplier.

79. A method for exchanging a plurality of resources among a plurality of entities as in claim 66 wherein said selection step comprises the step of conducting an auction among the entities.

80. A method for exchanging a plurality of resources among a plurality of entities as in claim 79 wherein said auction is a double oral auction.

81. A method for exchanging a plurality of resources among a plurality of entities as in claim 66 wherein said plurality of properties comprise at least one attribute.

82. A method for exchanging a plurality of resources among a plurality of entities as in claim 66 wherein said plurality of properties comprise at least one behavior.

83. A method for exchanging a plurality of resources among a plurality of entities as in claim 66 wherein said plurality of properties comprise at least one affordance.

84. A method for exchanging a plurality of Oresources among a plurality of entities as in claim 66 wherein said plurality of properties comprise at least one contract term.

85. A method for exchanging a plurality of resources among a plurality of entities as in claim 84 wherein said at least one contract term comprises an exchange time.

86. A method for exchanging a plurality of resources among a plurality of entities as in claim 84 wherein said at least one contract term comprises a quantity.

87. A method for exchanging a plurality of resources among a plurality of entities as in claim 84 wherein said at least one contract term comprises a price.

88. Computer executable software code stored on a computer readable medium, the code for exchanging a plurality of resources among a plurality of entities, the code comprising:

code to define a plurality of properties for the resources;

code to find at least one match among said properties of the resources to identify a plurality of candidate exchanges; and

code to select at least one exchange from said plurality of candidate exchanges.

89. Computer executable software code stored on a computer readable medium, the code for exchanging a plurality of resources among a plurality of entities as in claim 88, wherein the code to select at least one exchange further comprises:

code to define a joint satisfaction as at least one function of said plurality of properties to measure a mutual satisfaction of said candidate exchanges;

code to optimize said joint satisfaction to identify one or more of said candidate exchanges having an optimal mutual satisfaction; and

code to select said one or more of said candidate exchanges having an optimal mutual satisfaction.

90. Computer executable software code stored on a computer readable medium, the code for exchanging a plurality of resources among a plurality of entities as in claim 89, wherein the code to optimize said joint satisfaction further comprises:

code to decompose the joint satisfaction to minimize

$\underset{1<br><br>j<br><br>N+1}{\sum <br><br>}<br><br>f_j<br><br>\left({\mathrm{\chi <br><br>}}_j\right)$

 

subject to at least one constraint,

$-{\mathrm{\chi <br><br>}}_{N+1}+\underset{1<br><br>j<br><br>N}{\sum <br><br>}<br><br>{\mathrm{\chi <br><br>}}_j<br><br>v_j=0$

wherein;

X_j=P_j and X_N+1=Σ

1≦

j≦

N^x_jv_j are new coordinates for jε

[1,N] and j=N+1 respectively;

f_j (X_j)=s^C_j(X_j|v_j) and f_N+1(X₊₁)=s^L(X_N+1) are new functions for jε

[1,N] and j=N+1 respectively;

s^C_j(X_j|v_j) is a satisfaction of one of the entities participating in said candidate exchange and S^L(X_N+1) is a satisfaction of another of the entities participating in said candidate exchange.

91. A programmed computer for exchanging a plurality of resources among a plurality of entities, comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory, wherein the program code includes:

code to define a plurality of properties for the resources;

code to find at least one match among said properties of the resources to identify a plurality of candidate exchanges; and

code to select at least one exchange from said plurality of candidate exchanges.

92. A programmed computer for exchanging a plurality of resources among a plurality of entities, comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 91, wherein the code to select at least one exchange further includes:

code to define a joint satisfaction as at least one function of said plurality of properties to measure a mutual satisfaction of said candidate exchanges;

code to optimize said joint satisfaction to identify one or more of said candidate exchanges having an optimal mutual satisfaction; and

code to select said one or more of said candidate exchanges having an optimal mutual satisfaction.

93. A programmed computer for exchanging a plurality of resources among a plurality of entities, comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 91, wherein the code to optimize said joint satisfaction further includes:

code to decompose the joint satisfaction to minimize

$\underset{l<br><br>j<br><br>N+1}{\sum <br><br>}<br><br>f_j<br><br>\left({\mathrm{\chi <br><br>}}_j\right)$

 

subject to at least one constraint,

$-{\mathrm{\chi <br><br>}}_{N+1}+\underset{1<br><br>j<br><br>N}{\sum <br><br>}<br><br>{\mathrm{\chi <br><br>}}_j<br><br>v_j=0$

wherein;

X_j=P_j and X_N+1=Σ

1≦

j≦

N^X_jv_j are new coordinates for jε

[1,N] and j=N+1 respectively;

f_j(X_j)=s^C_j(X_j|v_j) and f_N+1(X_N+1)=S^L(X_N+1) are new functions for jε

[1,N] and j=N+1 respectively;

s^C_j (X_j|v_j) is a satisfaction of one of the entities participating in said candidate exchange and s^L(X_N+1) is a satisfaction of another of the entities participating in said candidate exchange.

94. A system for matching service requests with service offers comprising:

a request input device for receiving a plurality of service request preferences;

an offer input device for receiving a plurality of service offer preferences;

a computer storage system for storing evaluation criteria; and

a matching module configured to communicate with said request input device, said offer input device and said computer storage system for matching one or more of the service requests with one or more of the service offers.

95. A system for matching service requests with service offers as in claim 94 wherein said evaluation criteria comprise:

request evaluation criteria and

offer evaluation criteria.

96. A system for matching service requests with service offers as in claim 95 wherein said matching module comprises:

a first ranking module for ranking the service requests with respect to said request evaluation criteria and for selecting at least one of the service requests having a maximal rank;

an identification module for identifying one or more of the service offers that are compatible with said at least one of the service requests having a maximal rank;

a second ranking module for ranking said compatible service offers with respect to said offer evaluation criteria and for selecting at least one of said compatible service offers having a maximal rank; and

a price calculation module for setting a price for an exchange of said at least one selected service request and said at least one selected service offer.

97. A system for matching service requests with service offers as in claim 94 wherein said plurality of service request preferences are specified by at least one producer who has an opportunity to move one or more products.

98. A system for matching service requests with service offers as in claim 94 wherein said plurality of service offer preferences are specified by at least one distribution service provider.

99. A system for matching service requests with service offers as in claim 94 wherein said plurality of service request preferences comprise a maximum price.

100. A system for matching service requests with service offers as in claim 99 wherein said plurality of service request preferences further comprise a departure time and an arrival time.

101. A system for matching service requests with service offers as in claim 100 wherein said plurality of service request preferences further comprise a departure location and an arrival location.

102. A system for matching service requests with service offers as in claim 95 wherein said request evaluation criteria comprise a plurality of first weighting factors corresponding to said plurality of service request preferences.

103. A system for matching service requests with service offers as in claim 95 wherein said offer evaluation criteria comprise a plurality of second weighting factors corresponding to said plurality of service offer preferences.

104. A system for matching service requests with service offers as in claim 96 wherein said price calculation module comprises at least one price calculation expression.

105. A system for matching service requests with service offers as in claim 104 wherein said at least one price calculation expression comprises a plurality of shipping factors.

106. A system for matching service requests with service offers as in claim 105 wherein said shipping factors comprise shipping material, shipping volume, shipping weight, shipping time and shipping location.

107. A method for matching service requests with service offers comprising the steps of:

receiving a plurality of service request preferences with a request input device;

receiving a plurality of service offer preferences an offer input device;

storing evaluation criteria with a computer storage system; and

matching one or more of the service requests with one or more of the service offers with a matching module configured to communicate with said request input device, said offer input device and said computer storage system.

108. A method for matching service requests with service offers as in claim 107 wherein said evaluation criteria comprise:

request evaluation criteria and

offer evaluation criteria.

109. A method for matching service requests with service offers as in claim 108 wherein said matching one or more of the service requests with one or more of the service offers comprises the steps of:

ranking the service requests with respect to said request evaluation criteria;

selecting at least one of the service requests having a maximal rank;

identifying one or more of the service offers that are compatible with said at least one of the service requests having a maximal rank;

ranking said compatible service offers with respect to said offer evaluation criteria;

selecting at least one of said compatible service offers having a maximal rank; and

setting a price for an exchange of said at least one selected service request and said at least one selected service offer.

110. A method for matching service requests with service offers as in claim 107 wherein said plurality of service request preferences are specified by at least one producer who has an opportunity to move one or more products.

111. A method for matching service requests with service offers as in claim 107 wherein said plurality of service offer preferences are specified by at least one distribution service provider.

112. A method for matching service requests with service offers as in claim 107 wherein said plurality of service request preferences comprise a maximum price.

113. A method for matching service requests with service offers as in claim 112 wherein said plurality of service request preferences further comprise a departure time and an arrival time.

114. A method for matching service requests with service offers as in claim 113 wherein said plurality of service request preferences further comprise a departure location and an arrival location.

115. A method for matching service requests with service offers as in claim 114 wherein said plurality of service request preferences further comprise an arrival location.

116. A method for matching service requests with service offers as in claim 109 wherein said request evaluation criteria comprise a plurality of first weighting factors corresponding to said plurality of service request preferences.

117. A method for matching service requests with service offers as in claim 109 wherein said offer evaluation criteria comprise a plurality of second weighting factors corresponding to said plurality of service offer preferences.

118. A method for matching service requests with service offers as in claim 110 wherein said setting a price for an exchange step comprises the step of evaluating at least one price calculation expression.

119. A method for matching service requests with service offers as in claim 118 wherein said at least one price calculation expression comprises a plurality of shipping factors.

120. A method for matching service requests with service offers as in claim 119 wherein said shipping factors comprise shipping material, shipping volume, shipping weight, shipping time and shipping location.

122. Computer executable software code stored on a computer readable medium, the code for matching service requests with service offers as in claim 121 wherein said evaluation criteria comprise:

request evaluation criteria and

offer evaluation criteria.

123. Computer executable software code stored on a computer readable medium, the code for matching service requests with service offers as in claim 122 wherein said code to match one or more of the service requests with one or more of the service offers further comprises:

code to rank the service requests with respect to said request evaluation criteria;

code to select at least one of the service requests having a maximal rank;

code to identify one or more of the service offers that are compatible with said at least one of the service requests having a maximal rank;

code to rank said compatible service offers with respect to said offer evaluation criteria;

code to select at least one of said compatible service offers having a maximal rank; and

code to set a price for an exchange of said at least one selected service request and said at least one selected service offer.

125. A programmed computer for matching service requests with service offers, comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 124, wherein said evaluation criteria comprise:

request evaluation criteria and

offer evaluation criteria.

126. A programmed computer for matching service requests with service offers, comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 125, wherein said code to match one or more of the service requests with one or more of the service offers further comprises:

code to rank the service requests with respect to said request evaluation criteria;

code to select at least one of the service requests having a maximal rank;

code to identify one or more of the service offers that are compatible with said at least one of the service requests having a maximal rank;

code to rank said compatible service offers with respect to said offer evaluation criteria;

code to select at least one of said compatible service offers having a maximal rank; and

code to set a price for an exchange of said at least one selected service request and said at least one selected service offer.

128. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 127 further comprising the steps of:

characterizing the fitness landscape from said values of said hyperparameters; and

selecting at least one optimization technique that is suited to said characterization.

129. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 127 wherein one or more of said N variables are discrete.

130. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 129 wherein said covariance function has N first hyperparameters ρ

_l, i=1 . . . N corresponding to the N dimensions of said search space, each of said first hyperparameters representing the degree of correlation along said corresponding dimension in the fitness landscape.

131. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 129 wherein said covariance function comprises at least one stationary term.

132. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 131 wherein said covariance function is defined as:

C(x^(l),x^(j),Θ

)=Θ

₁C_s(x^(l),x^(j))+Θ

₂+δ

_i,jΘ

₃

wherein

133. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 132 wherein said learning step comprises the steps of:

simulating the system with a plurality of values of the input vector x;

observing the value of the output, y corresponding to said plurality of values of the inputs vectors x to generate the observed data, D={x⁽¹⁾, y⁽¹⁾, . . . , x^(d), y^(d)} wherein x^(l), y^(l) are values of the input vector x and the output y respectively.

134. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 133 wherein said learning step further comprises the step of generating a covariance matrix C_d(Θ

) from the observed data D={x⁽¹⁾, y⁽¹⁾, . . . , x^(d), y^(d)} and said covariance function.

135. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 134 wherein the (i,j) elements of said covariance matrix is defined as C(x^(l), x^(j), Θ

).

136. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 135 wherein said learning step further comprises the step of defining at least one likelihood function L(Θ

) that expresses the probability of the observed data D={x⁽¹⁾, y⁽¹⁾, . . . , x^(d), y^(d)} for different values of said hyperparameters.

137. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 136 wherein said learning step further comprises the step of determining values of the hyperparameters that maximize the logarithm of said likelihood function.

138. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 137 wherein the logarithm of said likelihood function is defined as:

L(Θ

)=−

½

log det C_d(Θ

)−

½

Y C_d<sup>−

1</sup>(Θ

) wherein log det C_d(Θ

) is the determinant of C_d(Θ

).

139. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 136 wherein said learning step further comprises the steps of:

defining at least one prior probability density function for said hyperparameters expressing probabilities of said possible values of said hyperparameters from the prior knowledge of the system; and

defining at least one posterior probability density function as a product of said at least one prior probability density function and said at least one likelihood function L(Θ

) wherein said posterior probability density function express the probabilities of possible values of said hyperparameters from the prior knowledge of the system and the observed data, D={x⁽¹⁾, y⁽¹⁾, . . . , x^(d), y^(d)}.

140. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 139 wherein said learning step further comprises the step of selecting one or more of the values of said hyperparameters having the greatest probability.

141. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 139 wherein said at least one prior probability density function is tunable.

142. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 139 wherein said at least one prior probability density function for said second hyperparameters is the gamma distribution.

143. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 139 wherein said at least one prior probability density function for said second hyperparameters is the inverse gamma distribution.

144. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 139 wherein said at least one prior probability density function for said first hyperparameters is a beta distribution.

145. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 139 wherein said at least one prior probability density function for said first hyperparameters is a modified beta distribution to include the possibility of negative values for said first hyperparameters.

146. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 129 wherein said discrete variables are binary variables.

147. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 146 wherein said distance between values of said binary variables is the Hamming distance.

149. Computer executable software code stored on a computer readable medium, the code for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 148, the code further comprising:

code to characterize the fitness landscape from said values of said hyperparameters; and

code to select at least one optimization technique that is suited to said characterization.

151. A programmed computer for optimizing a system by constructing a fitness landscape for the system from observed data, comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 150, wherein the program code further includes:

code to characterize the fitness landscape from said values of said hyperparameters; and

code to select at least one optimization technique that is suited to said characterization.

153. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 152 further comprising the steps of:

characterizing the fitness landscape from said values of said hyperparameters; and

selecting at least one optimization technique that is suited to said characterization.

154. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 152 wherein one or more of said N variables are discrete.

155. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 152 wherein said covariance functions have N first hyperparameters ρ

_l, i=1 . . . N corresponding to the N dimensions of said search space, each of said first hyperparameters representing the degree of correlation along said corresponding dimension in the fitness landscape.

156. A method for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 152 wherein said covariance function comprises at least one stationary term.

158. Computer executable software code stored on a computer readable medium, the code for optimizing a system by constructing a fitness landscape for the system from observed data as in claim 157, the code further comprising:

code to characterize the fitness landscape from said values of said hyperparameters; and

code to select at least one optimization technique that is suited to said characterization.

160. A programmed computer for optimizing a system by constructing a fitness landscape for the system from observed data, comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 159, wherein the program code further includes:

code to characterize the fitness landscape from said values of said hyperparameters; and

code to select at least one optimization technique that is suited to said characterization.

162. A method for performing operations management in an environment of entities and resources as in claim 161 wherein said determining a sparse representation of said discrete landscape step further comprises the steps of:

initializing a basis for said sparse representation;

defining an energy function comprising at least one error term to measure the error of said sparse representation and comprising at least one sparseness term to measure the degree of sparseness of said sparse representation; and

modifying said basis by minimizing said energy function such that said sparse representation has a minimal error and a maximal degree of sparseness.

163. A method for performing operations management in an environment of entities and resources as in claim 162 wherein said energy function is defined as:

167. A method for performing operations management as in claim 166 wherein said characterizations of said landscape representation comprise a first category wherein said landscape representations belonging to said first category do not contain any of said acceptable operations management solutions.

168. A method for performing operations management as in claim 167 wherein said characterizations of said landscape representation comprise a second category wherein said landscape representations belonging to said second category contain isolated areas of said acceptable operations management solutions.

169. A method for performing operations management as in claim 168 wherein said characterizations of said landscape representation comprise a third category wherein said landscape representations belonging to said second category contain connected webs of said acceptable operations management solutions.

170. A method for performing operations management as in claim 166 wherein said factors effecting said characterization of said landscape representation comprise at least one constraint.

171. A method for performing operations management as in claim 170 wherein said at least one constraint comprises a maximum allowable makespan.

172. A method for performing operations management as in claim 170 wherein said adjusting said at least one factor to facilitate an identification of at least one acceptable operations management solution step comprises the step of easing said at least one constraint.

173. A method for performing operations management as in claim 171 wherein said adjusting said at least one factor to facilitate an identification of at least one acceptable operations management solution step comprises the step of increasing said maximum allowable makespan.

174. A method for performing operations management as in claim 171 wherein said characterizing said landscape representation step comprises the steps of:

(a) selecting an initial point on said landscape representation;

(b) initializing a sampling distance;

(c) sampling said landscape representation at a plurality of points at said sampling distance from said initial point;

(d) computing the percentage of said sampled points qualifying as said at least one acceptable operations management solution;

(e) incrementing said sampling distance;

(f) repeating steps (c)-(e) for a plurality of iterations to compute a corresponding plurality of said percentages of acceptable operations management solutions;

(g) selecting one of said plurality of iterations;

(h) computing the logarithm of the ratio of said percentage of acceptable operations management solutions at said selected iteration to said percentage of acceptable operations management solutions at said iteration preceding said selected iteration;

(i) repeating said steps (g)-(h) for each of said plurality of iterations to compute a corresponding plurality of said ratios;

(j) repeating steps (a)-(i) for a plurality of said initial points to compute said plurality of said ratios for each of said initial points; and

(k) characterizing said landscape representation according to said ratios of said acceptable operations management solutions.

178. A method for performing multi-objective optimization as in claim 177 wherein said sampling said n dimensional energy function step comprises the steps of:

noting a lowest sampled energy value e; and

noting a highest sampled energy value {overscore (e)}.

179. A method for performing multi-objective optimization as in claim 178 wherein each of said c intervals I_i, i=0 . . . c−

1 include a portion of said energy values falling within an energy interval definition;

e+iδ

≦

e<

e+(i+1)δ

wherein

δ

=({overscore (e)}−

e)/c.

180. A method for performing multi-objective optimization as in claim 177 wherein said c intervals, I_l, i=0 . . . c−

1 overlap.

181. A method for performing multi-objective optimization as in claim 180 wherein said grouping said plurality of observed energy values step comprises the steps of:

identifying subsets of said c intervals I_i, i=0 . . . c−

1 having an overlap greater than a predetermined threshold; and

sliding said overlapping subsets to smooth the time series corresponding to said plurality of sampled energy values.

182. A method for performing multi-objective optimization as in claim 177 wherein said at least one estimated probability density function P_Il comprises at least one parameter θ

.

183. A method for performing multi-objective optimization as in claim 182 wherein said estimating at least one probability density function P_Il step comprises the step of estimating said at least one parameter θ

from said plurality of sampled energy values.

184. A method for performing multi-objective optimization as in claim 183 wherein said at least one parameter θ

is estimated using a learning algorithm.

185. A method for performing multi-objective optimization as in claim 177 wherein said searching for at least one low energy solution step comprises the steps of:

(a) initializing a set of known probability density functions to said plurality of estimated probability density functions P_Il;

(b) identifying at least one low energy interval I_

by extrapolating from said set of known probability density functions wherein said at least one low energy interval I_

contains at least one energy value which is lower than said plurality of sampled energy values;

(c) determining at least one low energy probability density function P_I

corresponding to said at least one low energy interval I_

by extrapolating from said set of known probability density functions;

(d) adding said at least one low energy probability density function P_I

to said set of known probability density functions; and

(e) repeating steps (b)-(d) until said at least one low energy interval I_

contains said at least one low energy solution.

186. A method for performing multi-objective optimization as in claim 185 wherein said at least one low energy probability density function P_I

comprises said at least one parameter θ

.

187. A method for performing multi-objective optimization as in claim 186 wherein said determining at least one low energy probability density function P_I

step comprises the step of extrapolating said at least one parameter θ

from said known probability density functions.

191. A method for interacting with a computer to perform multi-objective optimization as in claim 190 further comprising the step of:

adjusting said design entry controls on said design entry window to form at least one modification to zero or more of said variables and to zero or more of said objectives.

192. A method for interacting with a computer to perform multi-objective optimization as in claim 191 further comprising the step of:

reissuing said at least one design output command to cause the application to produce and to display at least one effect of said at least one modification on said at least one solution.

193. A method for interacting with a computer to perform multi-objective optimization as in claim 190 wherein said manipulating said design entry controls step also defines zero or more constraints on at least one of said variables and on at least one of said objectives.

194. A method for interacting with a computer to perform multi-objective optimization as in claim 193 wherein said issuing said at least one design output command from the application step causes the application to display at least one design output window including a plurality of design output controls.

195. A method for interacting with a computer to perform multi-objective optimization as in claim 194 further comprising the step of:

manipulating said design output controls on said design output window to define at least one format for said at least one solution.

196. A method for interacting with a computer to perform multi-objective optimization as in claim 195 further comprising the step of:

issuing at least one display output command from the application to cause the application to display said at least one solution in said at least one format.

197. A method for interacting with a computer to perform multi-objective optimization as in claim 193 wherein said zero or more constraints comprises at least one allowable range on said at least one variable and on said at least one objective.

198. A method for interacting with a computer to perform multi-objective optimization as in claim 197 wherein said at least one displayed solution comprises:

at least one variable representation corresponding to said at least one variable;

at least one objective representation corresponding to said at least one objective; and

zero or more constraint representations corresponding to said zero or more constraints.

199. A method for interacting with a computer to perform multi-objective optimization as in claim 198 wherein said at least one variable representation and said at least one objective representation are bar representations.

200. A method for interacting with a computer to perform multi-objective optimization as in claim 199 wherein

said at least one objective representation is a first color or a second color when said at least one corresponding objective is satisfied or said at least one corresponding objective is not satisfied respectively.

201. A method for interacting with a computer to perform multi-objective optimization as in claim 200 wherein

said at least one constraint representation is a mark on said at least one objective representation.

202. A method for interacting with a computer to perform multi-objective optimization as in claim 197 wherein said manipulating said design entry controls step further comprises the step of:

selecting at least one of said plurality of objectives for optimization.

203. A method for interacting with a computer to perform multi-objective optimization as in claim 202 wherein said issuing said at least one design output command from the application step causes the application to optimize said at least one solution with respect to said selected objectives.

204. A method for interacting with a computer to perform multi-objective optimization as in claim 195 wherein said manipulating said design output controls on said design output window to define at least one format step comprises the steps of:

identifying at least one of said objectives to plot in at least one histogram; and

specifying at least one number of bins corresponding to said at least one histogram.

205. A method for interacting with a computer to perform multi-objective optimization as in claim 204 wherein said issuing said at least one design output command from the application step causes the application to display said at least one solution comprising said at least one histogram having said corresponding number of bins.

206. A method for interacting with a computer to perform multi-objective optimization as in claim 205 wherein said at least one solution is partitioned in said at least one histogram according to whether or not said at least one solution meets said at least one constraint.

207. A method for interacting with a computer to perform multi-objective optimization as in claim 195 wherein said manipulating said design output controls on said design output window to define at least one format step comprises the steps of:

identifying at least one of said objectives to use for pareto optimization; and

selecting two or more of said variables and objectives to plot on at least one scatterplot.

208. A method for interacting with a computer to perform multi-objective optimization as in claim 207 wherein said issuing said at least one design output command from the application step causes the application to perform pareto optimization with respect to said identified objectives.

209. A method for interacting with a computer to perform multi-objective optimization as in claim 208 wherein said issuing at least one design output command step causes the application to display said at least one scatterplot having at least one point corresponding to said at least one solution.

210. A method for interacting with a computer to perform multi-objective optimization as in claim 209 wherein said issuing said at least one design output command from the application step causes the application to identify zero or more of said solutions which are pareto optimal.

211. A method for interacting with a computer to perform multi-objective optimization as in claim 210 wherein

said manipulating said design output controls on said design output window to define at least one format step further comprises the step of:

specifying at least one allowable range for at least one of said variables and said objectives.

212. A method for interacting with a computer to perform multi-objective optimization as in claim 211 wherein said design output controls further comprise at least one slider labels for specifying said at least one allowable range for at least one of said variables and said objectives.

213. A method for interacting with a computer to perform multi-objective optimization as in claim 211 wherein said issuing at least one design output command from the application step causes the application to identify zero or more of said solutions on said at least one scatterplot which satisfy said at least one allowable range for at least one of said variables and said objectives.

214. A method for interacting with a computer to perform multi-objective optimization as in claim 213 wherein said manipulating said design output controls on said design output window to define at least one format step further comprises the step of:

adjusting said at least one allowable range for at least one of said variables and said objectives.

215. A method for interacting with a computer to perform multi-objective optimization as in claim 214 wherein said issuing at least one design output command from the application step causes the application to interactively display at least one effect of said adjusting said at least one allowable range for at least one of said variables and said objectives step on said at least one solution.

216. A method for interacting with a computer to perform multi-objective optimization as in claim 195 wherein said manipulating said design output controls on said design output window to define at least one format step comprises the steps of:

identifying at least one of said objectives to use for pareto optimization; and

selecting two or more of said variables and objectives to plot on at least one parallel coordinate plot.

217. A method for interacting with a computer to perform multi-objective optimization as in claim 216 wherein said issuing at least one design output command from the application step causes the application to display said at least one parallel coordinate plot having at least one line corresponding to said at least one solution.

218. A method for interacting with a computer to perform multi-objective optimization as in claim 217 wherein

said issuing at least one design output command from the application step causes the application to identify zero or more of said solutions which are pareto optimal.

121. Computer executable software code stored on a computer readable medium, the code for matching service requests with service offers, the code comprising:

code to receive a plurality of service request preferences with a request input device;

code to receive a plurality of service offer preferences an offer input device;

code to store evaluation criteria with a computer storage system; and

code to match one or more of the service requests with one or more of the service offers with a matching module configured to communicate with said request input device, said offer input device and said computer storage system.

124. A programmed computer for matching service requests with service offers, comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory, wherein the code comprises:

code to receive a plurality of service request preferences with a request input device;

code to receive a plurality of service offer preferences an offer input device;

code to store evaluation criteria with a computer storage system; and

code to match one or more of the service requests with one or more of the service offers with a matching module configured to communicate with said request input device, said offer input device and said computer storage system.

127. A method for optimizing a system by constructing a fitness landscape for the system from observed data comprising the steps of:

defining an N-dimensional search space with an input vector x of N variables, N is a natural number;

defining a distance between values of said input vector;

defining at least one output y;

defining a covariance function of said distance,

said covariance function having a plurality of hyperparameters; and

learning values of said plurality of hyperparameters from the observed data.

148. Computer executable software code stored on a computer readable medium, the code for optimizing a system by constructing a fitness landscape for the system from observed data, the code comprising:

code to define an N-dimensional search space with an input vector x of N variables, N is a natural number;

code to define a distance between values of said input vector;

code to define at least one output y;

code to define a covariance function of said distance, said covariance function having a plurality of hyperparameters; and

code to learn values of said plurality of hyperparameters from the observed data.

150. A programmed computer for optimizing a system by constructing a fitness landscape for the system from observed data, comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory, wherein the program code includes:

code to define an N-dimensional search space with an input vector x of N variables, N is a natural number;

code to define a distance between values of said input vector;

code to define at least one output y;

code to define a covariance function of said distance, said covariance function having a plurality of hyperparameters; and

code to learn values of said plurality of hyperparameters from the observed data.

152. A method for optimizing a system by constructing a fitness landscape for the system from observed data comprising the steps of:

defining an N-dimensional search space with an input vector x of N variables, N is a natural number;

defining a distance between values of said input vector;

defining an M-dimensional one output vector t;

defining a M×

M matrix of covariance function across said M-dimensional output vector t, each of said covariance functions having a plurality of hyperparameters; and

learning values of said plurality of hyperparameters from the observed data.

157. Computer executable software code stored on a computer readable medium, the code for optimizing a system by constructing a fitness landscape for the system from observed data, the code comprising:

code to define an N-dimensional search space with an input vector x of N variables;

code to define a distance between values of said input vector;

code to define an M-dimensional output vector t;

code to define an M×

M matrix of covariance functions across said M-dimensional output vector t, each of said covariance functions having a plurality of hyperparameters; and

code to learn values of said plurality of hyperparameters from the observed data.

159. A programmed computer for optimizing a system by constructing a fitness landscape for the system from observed data, comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory, wherein the program code includes:

code to define an N-dimensional search space with an input vector x of N variables;

code to define a distance between values of said input vector;

code to define an M-dimensional output vector t;

code to define an M×

M matrix of covariance functions across said M-dimensional output vector t, each of said covariance functions having a plurality of hyperparameters; and

code to learn values of said plurality of hyperparameters from the observed data.

161. A method for performing operations management in an environment of entities and resources comprising the steps of:

creating a discrete landscape representation for the operations management in the environment;

determining a sparse representation of said discrete landscape to identify at least one salient feature of said discrete landscape;

selecting at least one optimization algorithm from a set of optimization algorithms by matching said salient features to said set of optimization algorithms; and

executing said selected optimization algorithm to identify at least one good operations management solution over said landscape representation.

164. Computer executable software code stored on a computer readable medium, the code for performing operations management in an environment of entities and resources, the code comprising:

code to create a discrete landscape representation for the operations management in the environment;

code to determine a sparse representation of said discrete landscape to identify at least one salient feature of said discrete landscape;

code to select at least one optimization algorithm from a set of optimization algorithms by matching said salient features to said set of optimization algorithms; and

code to execute said selected optimization algorithm to identify at least one good operations management solution over said landscape representation.

165. A programmed computer for performing operations management in an environment of entities and resources comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory, wherein the program code includes:

code to create a discrete landscape representation for the operations management in the environment;

code to determine a sparse representation of said discrete landscape to identify at least one salient feature of said discrete landscape;

code to select at least one optimization algorithm from a set of optimization algorithms by matching said salient features to said set of optimization algorithms; and

code to execute said selected optimization algorithm to identify at least one good operations management solution over said landscape representation.

166. A method for performing operations management in an environment of entities and resources comprising the steps of:

creating a landscape representation of the operations management in the environment;

characterizing said landscape representation;

determining at least one factor effecting said characterization of said landscape representation;

adjusting said at least one factor to facilitate an identification of at least one acceptable operations management solution over said landscape representation; and

identifying said at least one acceptable operations management solution.

175. Computer executable software code stored on a computer readable medium, the code for performing operations management in an environment of entities and resources, the code comprising:

code to create a landscape representation of the operations management in the environment;

code to characterize said landscape representation;

code to determine at least one factor effecting said characterization of said landscape representation;

code to adjust said at least one factor to facilitate an identification of at least one acceptable operations management solution over said landscape representation; and

code to identify said at least one acceptable operations management solution.

176. A programmed computer for performing operations management in an environment of entities and resources comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory, wherein the program code includes:

code to create a landscape representation of the operations management in the environment;

code to characterize said landscape representation;

code to determine at least one factor effecting said characterization of said landscape representation;

code to adjust said at least one factor to facilitate an identification of at least one acceptable operations management solution over said landscape representation; and

code to identify said at least one acceptable operations management solution.

177. A method for performing multi-objective optimization comprising the steps of:

creating an n dimensional energy function having a domain and a codomain to define a landscape representation wherein n is a natural number;

sampling said n dimensional energy function at a plurality of points x ε

X from the domain to determine a corresponding plurality of sampled energy values from the codomain;

grouping said plurality of sampled energy values into c intervals I_i, i=0 . . . c−

1 wherein c is a natural number;

estimating at least one probability density functions P_Il corresponding to said c intervals I_l, i=0 . . . c−

1 from said plurality of sampled energy values; and

searching for at least one low energy solution having a value from the codomain below a predetermined threshold by extrapolating from said estimated probability density functions P_Il.

188. Computer executable software code stored on a computer readable medium, the code for performing multi-objective optimization, the code comprising:

code to create an n dimensional energy function having a domain and a codomain to define a landscape representation wherein n is a natural number;

code to sample said n dimensional energy function at a plurality of points x ε

X from the domain to determine a corresponding plurality of sampled energy values from the codomain;

code to group said plurality of sampled energy values into c intervals I_l, i=0 . . . c−

1 wherein c is a natural number;

code to estimate at least one probability density functions P_Il corresponding to said c intervals I_i, i=0 . . . c−

1 from said plurality of sampled energy values; and

code to search for at least one low energy solution having a value from the codomain below a predetermined threshold by extrapolating from said estimated probability density functions P_Il.

189. A programmed computer for performing multi-objective optimization comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory, wherein the program code includes:

code to create an n dimensional energy function having a domain and a codomain to define a landscape representation wherein n is a natural number;

code to sample said n dimensional energy function at a plurality of points x ε

X from the domain to determine a corresponding plurality of sampled energy values from the codomain;

code to group said plurality of sampled energy values into c intervals I_l, i=0 . . . c−

1 wherein c is a natural number;

code to estimate at least one probability density functions P_Il corresponding to said c intervals I_i, i=0 . . . c−

1 from said plurality of sampled energy values; and

code to search for at least one low energy solution having a value from the codomain below a predetermined threshold by extrapolating from said estimated probability density functions P_Il.

190. A method for interacting with a computer to perform multi-objective optimization comprising the steps of:

executing an application which includes at least one design entry command to define a plurality of variables and a plurality of objectives and at least one design output command to produce and to display at least one solution;

issuing said at least one design entry command from the application to cause the application to display at least one design window including a plurality of design entry controls;

manipulating said design entry controls on said design window to define said plurality of variables and said plurality of objectives; and

issuing said at least one design output command from the application to cause the application to produce and to display said at least one solution.