Probabilistic optimization of resource discovery, reservation and assignment

• US 9,164,801 B2
• Filed: 08/28/2013
• Issued: 10/20/2015
• Est. Priority Date: 06/08/2010
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Friedlander, Robert R., Kraemer, James R., Lougee, Robin, Osipov, Kirill M.
• Primary Examiner(s): Lee, Tammy
• Assistant Examiner(s): Zhao, Bing
• Application Number:
  US14/011,806
• Publication Number:
  US 20130346996A1
• Time in Patent Office: 783 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06F 9/5005 to service a request
• CPC Class Codes: G06F 9/505 considering the load
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS

Abstract

A processor-implemented method, system and/or computer program product allocates multiple resources from multiple organizations. A series of requests for multiple resources from multiple organizations is received. The multiple resources are required to accomplish a specific task, and each of the multiple resources is assigned a probability of consumption. Probabilities of availability of the multiple resources are then determined and transmitted to the organizations.

Claims

1. A method of allocating multiple resources, the method comprising:

receiving, by one or more processors, a series of requests for multiple resources from multiple organizations, wherein the multiple resources are required to accomplish a task, and wherein each of the multiple resources has been assigned a probability of consumption, wherein the probability of consumption is based on a resource acquisition authorization history of a person who is requesting the multiple resources;

creating, by one or more processors, multiple resource type requests by retrieving flags, from the series of requests, that identify resource types for each of the multiple resources;

matching and transmitting, by one or more processors, each of the multiple resource type requests to one or more optimal analytic engines, wherein each different optimal analytic engine uses a different analytic technique to produce one or more solution sets for locating and apportioning resources needed to satisfy a best solution to each of the multiple resource type requests, wherein the best solution complies with predefined parameters;

generating and labeling, by one or more processors, a unique thread for each solution set for each of the multiple resource type requests;

reassembling, by one or more processors, threads for best solutions to the multiple resource type requests into a synthetic event, wherein the synthetic event is an emulation of the task and a completion of the task to which the multiple resources are allocated, wherein the synthetic event matches a description of the task, and wherein the synthetic event maintains a probabilistic, time period sensitive inventory of resources, requests, and demand likelihood of fulfilling the multiple resource type requests, wherein the synthetic event describes which particular resources will be allocated to the task, when said particular resources will be allocated to the task, and a likelihood of said particular resources being allocated to the task; and

in response to a specific optimal analytic engine failing to produce the best solution, transmitting, by one or more processors, a specific resource type request to another optimal analytic engine.

2. The method of claim 1, further comprising:

in response to an organization accepting a probability of availability for a specific resource, allocating, by one or more processors, the specific resource to a customer.

3. The method of claim 1, wherein a first person having a first resource acquisition authorization history of always receiving approval for acquiring requested resources has a higher probability of consumption than a second person having a second resource acquisition authorization history of never receiving approval for acquiring requested resources.

4. The method of claim 1, further comprising:

in response to the specific optimal analytic engine failing to produce the best solution, locating additional resources for use by the specific optimal analytic engine.

5. The method of claim 1, wherein said each different optimal analytic engine is matched based on a probability of the task being performed.

6. The method of claim 1, further comprising:

defining, by one or more processors, a resource type for each of the multiple resources to create multiple resource type notifications, wherein the multiple resource type notifications are used to transmit a series of notifications of availabilities of the multiple resources.

7. The method of claim 1, wherein each of the multiple resources comprises multiple hierarchies of sub-resources that are required to accomplish the task.

8. The method of claim 1, wherein the multiple resources comprise funding from grants, and wherein the probability of consumption is further based on whether a particular organization has historically used funding from grants.

9. The method of claim 1, wherein the task is a medical research project, and wherein the multiple resources comprise tissue samples required for the medical research project.

10. The method of claim 1, wherein the task is medical treatment of a group of patients, and wherein the multiple resources comprise health care providers.

11. The method of claim 1, wherein the task is a medical research project, and wherein the multiple resources comprise hardware research equipment required for the medical research project.

12. A computer program product for allocating multiple resources, the computer program product comprising a non-transitory computer readable storage medium having program code embodied therewith, the program code readable and executable by a processor to:

receive a series of requests for multiple resources from multiple organizations, wherein the multiple resources are required to accomplish a task, and wherein each of the multiple resources has been assigned a probability of consumption, wherein the probability of consumption is based on a resource acquisition authorization history of a person who is requesting the multiple resources;

create multiple resource type requests by retrieving flags, from the series of requests, that identify resource types for each of the multiple resources;

match and transmit each of the multiple resource type requests to one or more optimal analytic engines, wherein each different optimal analytic engine uses a different analytic technique to produce one or more solution sets for locating and apportioning resources needed to satisfy a best solution to each of the multiple resource type requests, wherein the best solution complies with predefined parameters;

generate and label a unique thread for each solution set for each of the multiple resource type requests;

reassemble threads for best solutions to the multiple resource type requests into a synthetic event, wherein the synthetic event is an emulation of the task and a completion of the task to which the multiple resources are allocated, wherein the synthetic event matches a description of the task, and wherein the synthetic event maintains a probabilistic, time period sensitive inventory of resources, requests, and demand likelihood of fulfilling the multiple resource type requests, wherein the synthetic event describes which particular resources will be allocated to the task, when said particular resources will be allocated to the task, and a likelihood of said particular resources being allocated to the task; and

in response to a specific optimal analytic engine failing to produce the best solution, transmit a specific resource type request to another optimal analytic engine.

13. The computer program product of claim 12, wherein the program code is further readable and executable by a processor to:

in response to an organization accepting a probability of availability for a specific resource, allocate the specific resource to a customer.

14. The computer program product of claim 12, wherein a first person having a first resource acquisition authorization history of always receiving approval for acquiring requested resources has a higher probability of consumption than a second person having a second resource acquisition authorization history of never receiving approval for acquiring requested resources.

15. The computer program product of claim 12, wherein the program code is further readable and executable by a processor to:

define a resource type for each of the multiple resources to create multiple resource type notifications, wherein the multiple resource type notifications are used to transmit a series of notifications of availabilities of the multiple resources.

16. The computer program product of claim 12, wherein the program code is further readable and executable by a processor to:

in response to the specific optimal analytic engine failing to produce the best solution, locate additional resources for use by the specific optimal analytic engine.

17. A computer system comprising:

a central processing unit (CPU), a computer readable memory, and a computer readable storage media;

first program instructions to receive a series of requests for multiple resources from multiple organizations, wherein the multiple resources are required to accomplish a task, and wherein each of the multiple resources has been assigned a probability of consumption, wherein the probability of consumption is based on a resource acquisition authorization history of a person who is requesting the multiple resources;

second program instructions to create multiple resource type requests by retrieving flags, from the series of requests, that identify resource types for each of the multiple resources;

third program instructions to match and transmit each of the multiple resource type requests to one or more optimal analytic engines, wherein each different optimal analytic engine uses a different analytic technique to produce one or more solution sets for locating and apportioning resources needed to satisfy a best solution to each of the multiple resource type requests, wherein the best solution complies with predefined parameters;

fourth program instructions to generate and label a unique thread for each solution set for each of the multiple resource type requests;

fifth program instructions to reassemble threads for best solutions to the multiple resource type requests into a synthetic event, wherein the synthetic event is an emulation of the task and a completion of the task to which the multiple resources are allocated, wherein the synthetic event matches a description of the task, and wherein the synthetic event maintains a probabilistic, time period sensitive inventory of resources, requests, and demand likelihood of fulfilling the multiple resource type requests, wherein the synthetic event describes which particular resources will be allocated to the task, when said particular resources will be allocated to the task, and a likelihood of said particular resources being allocated to the task; and

sixth program instructions to, in response to a specific optimal analytic engine failing to produce the best solution, transmit a specific resource type request to another optimal analytic engine; and

wherein

the first, second, third, fourth, fifth, and sixth program instructions are stored on the non-transitory computer readable storage media for execution by the CPU via the computer readable memory.

18. The computer system of claim 17, further comprising:

seventh program instructions to, in response to an organization accepting a probability of availability for a specific resource, allocate the specific resource to a customer; and

wherein

the seventh program instructions are stored on the computer readable storage media for execution by the CPU via the computer readable memory.

19. The computer system of claim 17, wherein a first person having a first resource acquisition authorization history of always receiving approval for acquiring requested resources has a higher probability of consumption than a second person having a second resource acquisition authorization history of never receiving approval for acquiring requested resources.