Resource allocation system and method

• US 4,797,839 A
• Filed: 01/12/1987
• Issued: 01/10/1989
• Est. Priority Date: 08/13/1983
• Status: Expired due to Fees

• Current Assignee: British Aerospace, Inc.
• Original Assignee: British Aerospace, Inc.
• Inventors: Powell, John H.
• Primary Examiner(s): Lall, Parshotam S.
• Assistant Examiner(s): Cosimano, Edward R.
• Application Number:
  US07/004,853
• Time in Patent Office: 729 Days
• Field of Search: 364/400, 364/401, 364/402, 364/403, 364/423, 364/554
• US Class Current: 700/90
• CPC Class Codes: F41H 11/00 Defence installations; Defence devices constructional aspects see Section E, e.g. air-raid shelters E04H9/04; protective arrangements for buildings E04B1/92; extinguishing or preventing the spread of fire from, incendiary bombs A62C3/06; dynamic armour F41H5/007; ballistically deployed systems for restraining persons or animals F41H13/0006; electronic countermeasures G01S
• CPC Class Codes: G06F 17/10 Complex mathematical operations function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

A resource allocation system for indicating the optimum allocation of a resource between a series of demands iteratively processes the probability data for each demand to determine for each of a range of amounts of resource available the optimum distribution of the resource between the demands and the resultant probability of successfully overcoming all the demands. The system may be used to predict the probability of success where there is a limited resource available and a predicted series of demands, but it may also be used to assess non-predicted threats and to determine an updated optimum distribution and resultant probability.

Claims

1. A resource allocation system for optimally allocating a physical source among a series of demands on said physical resource, said system comprising:

resource allocation computing means having memory means, said resource allocation computing means;

(i) determining demand success probability data for each demand,

(ii) combining the demand success probability data of two of said demands, thereby to determine the optimum success probability data for the two demands combined and the optimum distribution data for the two demands combined,

(iii) storing the optimum success probability data and the optimum distribution data in said memory means,

(iv) selecting the demand success probability data relating to further demands in turn and processing this data, together with the optimum success probability data for the previous combination of demands previously determined, to determine updated optimum success probability data for the further demand and the previous combination and to determine the optimum distribution data for the further demand and the previous combination, and

(v) repeating said selecting and processing so as to process the data for the remaining demands and so as to determine for a given amount of said physical resource the optimum allocation of the physical resource among the demands together with the resultant probability of successfully overcoming the demands, and

means connected to said resource allocation computing means for physically indicating how the physical resource should be optimally allocated in response to said demands.

2. A resource allocation system as claimed in claim 1, wherein the optimum distribution of a physical resource X among a series of demands 1 . . . N is determined by said resource allocation computing means by combining the demand success probability data according to the following algorithm:

##EQU8## where F*_n (X) is the optimum success probability data for the combination of demands n to N inclusive;

P_k .sbsb.n(x_n) is the probability of failing successfully to overcome present demand n given allocation of x_n resource; and

F*_n+1 (X-x_n) is the optimum success probability data for the combination of demands n+1 to N inclusive.

3. A resource allocation system as claimed in claim 1, further including data base means for storing demand success probability data for a plurality of different demands, and demand sensor module means for sensing and identifying a demand and for causing the appropriate demand success probability data to be supplied to said resource allocation computing means for processing.

4. A method of optimally allocating a physical resource among a series of demands on said physical resource, said method including the steps of:

(i) determining for each demand the demand success probability data,

(ii) combining the demand success probability data for two demands, thereby to determine the optimum success probability data for the two demands combined and the optimum distribution data for the combined demands,

(iii) storing the optimum success probability data and the optimum distribution data,

(iv) combining the optimum success probability data for the two combined demands with the demand success probability data of a further demand thereby to determine the optimum success probability data and the optimum distribution data for the combination of said two demands and said further demand,

(v) continuing said combination of optimum success probability data with remaining demand success probability data in turn until the optimum success probability data and the optimum distribution data for the combination of all the demands has been determined, and

(vi) physically indicating how the physical resource should be optimally allocated in response to said demands according to said determination.

5. A method in accordance with claim 4, wherein the combination of demand success probability data in steps (ii), (iv) and (v) for a series of demands 1 . . . N is determined by combining the demand success probability data according to the following algorithm:

##EQU9## where F*_n (X) is the optimum success probability data for the combination of demands n to N inclusive;

P_k.sbsb.n (x_n) is the probability of failing successfully to overcome present demand n given allocation of x_n resource;

F*_n+1 (X-x_n) is the optimum success probability data for the combination of demands n+1 to N inclusive.

6. A resource allocation system for controlling the optimum allocation of a physical resource among a series of demands on said physical resource, said system comprising:

resource allocation computing means having memory means, said resource allocation computing means;

(i) determining demand success probability data for each demand,

(ii) combining the demand success probability data of two of said demands, thereby to determine the optimum success probability data for the two demands combined and the optimum distribution data for the two demands combined,

(iii) storing the optimum success probability data and the optimum distribution data in said memory means,

(iv) selecting the demand success probability data relating to further demands in turn and processing this data, together with the optimum success probability data for the previous combination of demands previously determined, to determine updated optimum success probability data for the further demand and the previous combination and to determine the optimum distribution data for the further demand and the previous combination, and

(v) repeating said selecting and processing so as to process the data for the remaining demands and so as to determine for a given amount of said physical resource the optimum allocation of the physical resource among the demands together with the resultant probability of successfully overcoming the demands, and

means connected to said resource allocation computing means for controlling the allocation of said physical resource in accordance with the determined optimum allocation of said physical resource.

7. A resource allocation system as claimed in claim 6, wherein the optimum distribution of a physical resource X among a series of demands 1 . . . N is determined by said resource allocation computing means by combining the demand success probability data according to the following algorithm:

##EQU10## where F*_n (X) is the optimum success probability data for the combination of demands n to N inclusive;

P_k.sbsb.n (x_n) is the probability of failing successfully to overcome present demand n given allocation of x_n resource; and

F*_n+1 (X-x_n) is the optimum success probability data for the combination of demands n+1 to N inclusive.

8. A resource allocation system as claimed in claim 6, further including data base means for storing demand success probability data for a plurality of different demands, and demand sensor module means for sensing and identifying a demand and for causing the appropriate demand success probability data to be supplied to said resource allocation computing means for processing.

9. A method of controlling the optimum distribution of a physical resource among a series of demands on said physical resource, said method including the steps of:

(i) determining for each demand the demand success probability data,

(ii) combining the demand success probability data for two demands, thereby to determine the optimum success probability data for the two demands combined and the optimum distribution data for the combined demands,

(iii) storing the optimum success probability data and the optimum distribution data,

(iv) combining the optimum success probability data for the two combined demands with the demand success probability data of a further demand thereby to determine the optimum success probability data and the optimum distribution data for the combination of said two demands and said further demand,

(v) continuing said combination of optimum success probability data with remaining demand success probability data in turn until the optimum success probability data and the optimum distribution data for the combination of all the demands has been determined, and

(vi) controlling the distribution of said physical resource in an optimum manner according to said determination.

10. A method in accordance with claim 9, wherein the combination of demand success probability data in steps (ii), (iv) and (v) for a series of demands 1 . . . N is determined by combining the demand success probability data according to the following algorithm:

##EQU11## where F*_n (X) is the optimum success probability data for the combination of demands n to N inclusive;

P_k.sbsb.n (x_n) is the probability of failing successfully to overcome present demand n given allocation of x_n resource; and

F*_n+1 (X-x_n) is the optimum success probability data for the combination of demands n+1 to N inclusive.

11. A resource allocation system for indicating the optimum allocation of a physical resource among a series of demands on said physical resource, said system comprising:

demand sensor module means for sensing and identifying demands on said physical resource,

resource allocation computing means having memory means, said resource allocation computing means;

(i) determining demand success probability data for each demand identified by said demand sensor module means,

(ii) combining the demand success probability data of two of said demands, thereby to determine the optimum success probability data for the two demands combined and the optimum distribution data for the two demands combined,

(iii) storing the optimum success probability data and the optimum distribution data in said memory means,

(iv) selecting the demand success probability data relating to further demands in turn and processing this data, together with the optimum success probability data for the previous combination of demands previously determined, to determine updated optimum success probability data for the further demand and the previous combination and to determine the optimum distribution data for the further demand and the previous combination, and

(v) repeating said selecting and processing so as to process the data for the remaining demands and so as to determine for a given amount of said physical resource the optimum allocation of the physical resource among the demands together with the resultant probability of successfully overcoming the demands, and

means connected to said resource allocation computing means for physically indicating at least said optimum allocation of the physical resource.

12. A method of indicating the optimum distribution of a physical resource among a series of demands on said physical resource, said method including the steps of:

(i) sensing and identifying demands on said physical resource,

(ii) determining for each demand identified in said sensing and identifying step the demand success probability data,

(iii) combining the demand success probability data for two of said identified demands, thereby to determine the optimum success probability data for the two demands combined and the optimum distribution data for the combined demands,

(iv) storing the optimum success probability data and the optimum distribution data,

(v) combining the optimum success probability data for the two combined demands with the demand success probability data of a further demand thereby to determine the optimum success probability data and the optimum distribution data for the combination of said two demands and said further demand,

(vi) continuing said combination of optimum success probability data with remaining demand success probability data in turn until the optimum success probability data and the optimum distribution data for the combination of all the identified demands has been determined, and

(vii) physically indicating the distribution of the physical resource in an optimum manner according to said determination.

13. A resource allocation system for indicating to the pilot of an aircraft the optimum allocation of a resource of said aircraft among a series of demands on said resource, said system comprising:

resource allocation computing means having memory means, said resource allocation computing means;

(i) determining demand success probability data for each demand,

(ii) combining the demand success probability data of two of said demands, thereby to determine the optimum success probability data for the two demands combined and the optimum distribution data for the two demands combined,

(iii) storing the optimum success probability data and the optimum distribution data in said memory means,

(iv) selecting the demand success probability data relating to further demands in turn and processing this data, together with the optimum success probability data for the previous combination of demands previously determined, to determine updated optimum success probability data for the further demand and the previous combination and to determine the optimum distribution data for the further demand and the previous combination, and

(v) repeating said selecting and processing so as to process the data for the remaining demands and so as to determine for a given amount of said resource the optimum allocation of the resource among the demands together with the resultant probability of successfully overcoming the demands, and

means connected to said resource allocation computing means for physically indicating to said pilot how said resource should be optimally allocated by said pilot in response to said demands.

14. A resource allocation system as claimed in claim 13, wherein said resource is radar-jamming power.

15. A resource allocation system as claimed in claim 13, wherein said resource is a mission path.

16. A resource allocation system as claimed in claim 13, wherein said resource is the fuel supply of said aircraft.

17. A resource allocation system for optimally allocating an energy consuming resource among a series of demands on said energy consuming resource, said system comprising:

resource allocation computing means having memory means, said resource allocation computing means;

(i) determining demand success probability data for each demand,

(ii) combining the demand success probability data of two of said demands, thereby to determine the optimum success probability data for the two demands combined and the optimum distribution data for the two demands combined,

(iii) storing the optimum success probability data and the optimum distribution data in said memory means,

(iv) selecting the demand success probability data relating to further demands in turn and processing this data, together with the optimum success probability data for the previous combination of demands previously determined, to determine updated optimum success probability data for the further demand and the previous combination and to determine the optimum distribution data for the further demand and the previous combination, and

(v) repeating said selecting and processing so as to process the data for the remaining demands and so as to determine for a given amount of said energy consuming resource the optimum allocation of the energy consuming resource among the demands together with the resultant probability of successfully overcoming the demands, and

means connected to said resource allocation computing means for physically indicating how said energy consuming resource should be optimally allocated in response to said demands.

18. A method of optimally allocating an energy consuming resource among a series of demands on said energy consuming resource, said method including the steps of:

(i) determining for each demand the demand success probability data,

(ii) combining the demand success probability data for two demands, thereby to determine the optimum success probability data for the two demands combined and the optimum distribution data for the combined demands,

(iii) storing the optimum success probability data and the optimum distribution data,

(iv) combining the optimum success probability data for the two combined demands with the demand success probability data of a further demand thereby to determine the optimum success probability data and the optimum distribution data for the combination of said two demands and said further demand,

(v) continuing said combination of optimum success probability data with remaining demand success probability data in turn until the optimum success probability data and the optimum distribution data for the combination of all the demands has been determined, and

(vi) physically indicating how the energy consuming resource should be optimally allocated in response to said demands according to said determination.