Methods and apparatus for optimal resource allocation

Abstract

Method and apparatus to automatically allocate and schedule weapon systems to threats for maximizing an engagement objective. In one aspect, methods and systems maximize threat killed. In another aspect, methods and systems maximize asset survival against threats. The methods and apparatus considers temporal and resource constraints such that weapons systems are able to engage threats assigned to them.

1 . A method comprising:
 automatically allocating and scheduling resources to tasks for optimizing engagement objectives by:   modeling engagement resource and temporal constraints of the resources such that the resources are allocated to tasks that the resources are able to engage.   
     
     
         2 . The method according to  claim 1 , wherein the modeling of engagement resource and temporal constraints occurs during the allocating to determine when to deploy the allocated resources. 
     
     
         3 . The method according to  claim 1 , further including optimizing engagement times of the resources. 
     
     
         4 . The method according to  claim 1 , further including using a genetic algorithm having an engagement objective of optimizing the allocation of the resources to the tasks. 
     
     
         5 . The method according to  claim 4 , further including using simulated annealing, given the allocation of the resources to the tasks, to identify an optimal or near optimal schedule of resource engagements to maximize expected kills or assets saved. 
     
     
         6 . The method according to  claim 4 , further including using a heuristic algorithm for temporal optimization of the engagement objective. 
     
     
         7 . The method according to  claim 1 , further including determining whether active ones of the tasks can be can be engaged by at least one of the resources, and if not, determining a resource-task pairing that maximizes at least one objective function. 
     
     
         8 . The method according to  claim 1 , wherein the resources include weapons systems and the tasks include targets. 
     
     
         9 . The method according to  claim 1 , further including processing multiple objective functions. 
     
     
         10 . The method according to  claim 9 , further including using a genetic algorithm to process the multiple objective functions. 
     
     
         11 . The method according to  claim 10 , further including using a pareto-optimal genetic algorithm to simultaneously solve the multiple objective functions. 
     
     
         12 . A method, comprising:
 generating an initial population of option configurations for assignment of weapons systems to threats;   evaluating a fitness of the option configurations;   converting the option configurations to weapon assignment tables (WAT);   determining the fitness of the WATs; and   determining if temporal and resource constraints are satisfied for at least one of the WATs.   
     
     
         13 . The method according to  claim 12 , wherein the at least one WAT has the highest total threat value. 
     
     
         14 . The method according to  claim 12 , further including using a genetic algorithm to generate the initial population and iteratively generate additional populations. 
     
     
         15 . The method according to  claim 12 , further including using a heuristic to increase computation speed. 
     
     
         16 . A method, comprising:
 assigning resources to threats;   determining if constraints are satisfied for the resource-threat assignments; and   maximizing, for the resource-threat assignment that met the constraints, a first objective function.   
     
     
         17 . The method according to  claim 16 , further including maximizing the first objective function and a second objective function. 
     
     
         18 . The method according to  claim 17 , further including processing the first and second objective functions simultaneously. 
     
     
         19 . The method according to  claim 18 , further including using a pareto-optimal genetic algorithm. 
     
     
         20 . A system, comprising:
 a resource allocation and scheduling module to automatically allocate and schedule resources to tasks for optimizing engagement objectives by modeling engagement resource and temporal constraints of the resources such that the resources are allocated to tasks that the resources are able to engage.   
     
     
         21 . The system according to  claim 20 , wherein the modeling of engagement resource and temporal constraints occurs during the allocating to determine when to deploy the allocated resources. 
     
     
         22 . The system according to  claim 20 , further including optimizing engagement times of the resources. 
     
     
         23 . The system according to  claim 20 , wherein the resource allocation and scheduling module determines whether active ones of the tasks can be can be engaged by at least one of the resources, and if not, determines a resource-task pairing that maximizes at least one objective function. 
     
     
         24 . An article, comprising:
 a storage medium having stored thereon instructions that when executed by a machine result in the following:   automatically allocating and scheduling resources to tasks for optimizing engagement objectives by:   modeling engagement resource and temporal constraints of the resources such that the resources are allocated to tasks that the resources are able to engage.   
     
     
         25 . The article according to  claim 24 , wherein the modeling of engagement resource and temporal constraints occurs during the allocating to determine when to deploy the allocated resources. 
     
     
         26 . The article according to  claim 24 , further including instructions for optimizing engagement times of the resources. 
     
     
         27 . The article according to  claim 24 , further including instructions for using a genetic algorithm having an engagement objective of optimizing the allocation of the resources to the tasks. 
     
     
         28 . The article according to  claim 27 , further including instructions for using simulated annealing, given the allocation of the resources to the tasks, to identify an optimal or near optimal schedule of resource engagements to maximize expected kills or assets saved. 
     
     
         29 . The article according to  claim 24 , further including instructions for determining whether active ones of the tasks can be can be engaged by at least one of the resources, and if not, determining a resource-task pairing that maximizes at least one objective function. 
     
     
         30 . The article according to  claim 24 , wherein the resources include weapons systems and the tasks include targets.