Genetic smartjobs scheduling engine

Abstract

In a method for handling a plurality of heuristics for task selection in a genetic algorithm, a task scheduling engine generates a population of tasks associated with an overall objective, identifies multiple jobs associated with an overall objective, compiles the multiple jobs into a genome, and assigns one or more tasks to each job of the multiple jobs. The task scheduling engine also assigns a task heuristic byte defining multiple task heuristics that can be applied to the each job of the genome, randomly assigns a task heuristic from the multiple task heuristics to the each job, and determines a value score for the genome.

What is claimed is: 
     
         1 . A method for handling a plurality of heuristics for task selection in a genetic algorithm, comprising:
 generating a population of tasks associated with an overall objective;   identifying at least a first group of jobs and a second group of jobs associated with an overall objective;   compiling at least the first group of jobs and the second group of jobs into a genome;   assign one or more tasks to at least the first group of jobs and the second group of jobs;   assigning a task heuristic byte to the genome, wherein the task heuristic byte defines multiple task heuristics that can be applied to at least one job from at least one the first group of jobs and the second group of jobs;   randomly assigning a task heuristic from the multiple task heuristics to the at least one job from the at least the first group of jobs and the second group of jobs;   determining a value score for the genome using a fitness function; and   selecting a job for a task from the population of tasks based on the value score.   
     
     
         2 . The method of  claim 1 , wherein the task heuristic defines a selection criterion associated with the each job that is applied to the one or more tasks. 
     
     
         3 . The method of  claim 2 , wherein the task heuristic is selected from the group comprising; a most resource intensive task, a least resource intensive task, an earliest due date, a latest due date, a shortest completion time, a longest completion time, an alphabetically earliest name, an alphabetically latest name, a highest priority, and a lowest priority. 
     
     
         4 . The method of  claim 1 , wherein assigning the task heuristic byte to the first task genome further comprises assigning a single gene per job from the at least one job specifying the task heuristic. 
     
     
         5 . The method of  claim 1 , further comprising:
 associating the one or more tasks with the each job from the at least one job;   converting the one or more tasks associated with the at least one job into one or more multi-node tree graphs, wherein the at least one job is a root node and each task associated with the at least one job branches off of the root node.   
     
     
         6 . The method of  claim 1 , further comprising:
 identifying a second genome associated with the multiple task heuristics;   randomly selecting one or more jobs from at least one of the first group of jobs and the second group of jobs associated with the overall objective;   switching the placement of the one or more jobs between the genome and the second genome to create a recombined genome; wherein each job comprises the one or more tasks and is mirrored in both the genome and the second genome; and   determining a value score of the recombined genome.   
     
     
         7 . The method of  claim 1 , further comprising:
 randomly selecting a job from at least one of the first group and the second group of jobs;   determining the task heuristic and the tasks associated with the job;   randomly changing one or more variables associated with the job to create a mutated job; and   integrating the mutated job into the task genome in place of the job.   
     
     
         8 . The method of  claim 7 , wherein the one or more variables are selected from the group consisting of: tasks, task heuristics, an ordering of the tasks. 
     
     
         9 . A method for controlling one or more physical manufacturing resources, comprising:
 generating a population of resources;   identifying at least a first resource and a second resource associated with one or more jobs that comprise one or more tasks associated with an overall objective;   compiling at least the first resource and the second resource into a resource genome;   calculating resource utilization and overall resource time required to complete the one or more jobs based on the one or more tasks;   assigning a resource heuristic byte to the resource genome, wherein the resource heuristic byte defines multiple resource heuristics that can be applied to the one or more jobs;   randomly assigning a resource heuristic from the multiple resource heuristics to the each job; and   determine a value score for the genome.   
     
     
         10 . The method of  claim 9 , wherein the resource heuristic defines a selection criterion associated with the each resource that is applied to the one or more jobs. 
     
     
         11 . The method of  claim 10 , wherein the resource heuristic is selected from the group comprising; a most constrained resource, a least constrained resource, an earliest availability, a latest availability, a resource setup, a resource with the most utilization, and a resource with the least utilization. 
     
     
         12 . The method of  claim 9 , assigning the resource heuristic byte to the first resource genome further comprises assigning a single gene per resource specifying the resource heuristic. 
     
     
         13 . The method of  claim 9 , further comprising:
 identifying a second resource genome associated with the multiple resource heuristics;   randomly selecting one or more resources associated with the each job of the overall objective;   switching the placement of the one or more resources between the resource genome and the second resource genome to create a recombined resource genome; wherein each resource is used in both the resource genome and the second resource genome; and   determining a value score of the recombined resource genome.   
     
     
         14 . The method of  claim 9 , further comprising:
 randomly selecting a resource;   determining the resource heuristic and the resource associated with the job;   randomly changing one or more variables associated with the resource to create a mutated resource; and   integrating the mutated resource into the resource genome in place of the resource.