Abstract: A system and method for a using a genetic algorithm with an extra bit in the genome to obtain a best-fit of resource combinations for a particular task to be scheduled. The system can generate a genome from a plurality of jobs and then add an extra bit that can be used to find the best resource combination in the event that multiple resource combinations are equally suitable as an initial fit.

Abstract: A system includes memory configured to store image content representative of a lead implanted within a patient, and processing circuitry. The processing circuitry is configured to determine a reference point in the image content, determine a plane in the image content that corresponds to an orientation marker based on the reference point, determine an orientation of the lead based on the determined plane, and output information indicative of the determined orientation.

Abstract: A system includes memory configured to store image content representative of a lead implanted within a patient, and processing circuitry. The processing circuitry is configured to determine a reference point in the image content, determine a plane in the image content that corresponds to an orientation marker based on the reference point, determine an orientation of the lead based on the determined plane, and output information indicative of the determined orientation.

Abstract: A computer-implemented scheduling method for a plurality of jobs that utilizes a genetic algorithm can generate a non-linear weighted matrix of requirements that is based on a fitness function that can be modified according to requirements and applied to generate schedules for corresponding genomes made up of a plurality of jobs. The matrix can be used to account for factors that traditional computing systems applying genetic algorithm-based scheduling simply cannot, resulting in gains in efficiency for the computing systems of the inventive subject matter.

Abstract: Systems and methods in which a scheduling computing system reduces the computational load required by a computing device to generate a schedule by generating a schedule of tasks within a genetic algorithm using directional scheduling within the genetic algorithm. To do so, the computing device is programmed to obtain a plurality of jobs and determine whether the jobs can be backward scheduled. If so, the computing device fits the job, task by task, into the schedule via backward scheduling.

Abstract: A system for enforcing schedule barriers within a genetic algorithm includes one or more computing devices programmed to determine whether a job or a task within a job includes a hold that would prevent the execution of any tasks or jobs before that date/time, apply the hold as a virtual nowline within a generated genome and prevent the genetic algorithm from scheduling tasks before the virtual nowline. Having determined and enforced the virtual nowline, the system can proceed to execute tasks that start after the virtual nowline.

Abstract: A system for increasing the computational efficiency of a scheduling system applying genetic algorithms. A computing device of the scheduling system collects a plurality of jobs, each having a plurality of tasks. The computing device then proceeds to locate one or more fixed tasks among all of the collected tasks and removes the fixed tasks from the pool of tasks to be scheduled. The computing device blocks off time and resources associated with the fixed tasks, and then proceeds to generate a genome from the remaining tasks. The genomes are then used by a genetic algorithm executed by the computing device to generate a schedule.

Abstract: In a method for applying deterministic logic to select resources for resource genomes in a genetic algorithm, a logic engine identifies resources associated with an objective and an overall task population to be completed by one or more of the identified resources. The logic engine then selects a deterministic logical framework from one or more deterministic logical frameworks based on the objective. Following the selection of a deterministic logical framework, the logic engine selects one or more resources from the one or more identified resources based on the selected deterministic logical framework. The logic engine compiles the one or more selected resources into a resource genome, assigns one or more tasks from the task population to the one or more selected resources, and sends instructions to the one or more selected resources to execute the one or more tasks. The logic engine determines a value score for the resource genome.

Abstract: A method and apparatus for performing a bypass block navigational function to a user interface including generating, by a processor, a webpage including a bypass block navigation attribute associated with a first portion of the webpage, receiving, at a network interface a request for the webpage from the user interface, transmitting, by the network interface, the webpage and a software module including an instruction associated with the bypass block navigation function in response to the request, causing display, at the user interface, the webpage at a starting location, receiving, from the user interface, a keystroke associated with the bypass block navigation function, and causing display, at the user interface, of the first portion of the webpage in response to the instruction associated with the bypass block navigation function.

Abstract: A system includes memory configured to store image content representative of a lead implanted within a patient, and processing circuitry. The processing circuitry is configured to determine a reference point in the image content, determine a plane in the image content that corresponds to an orientation marker based on the reference point, determine an orientation of the lead based on the determined plane, and output information indicative of the determined orientation.

Abstract: A method and apparatus for performing a bypass block navigational function to a user interface including generating, by a processor, a webpage including a bypass block navigation attribute associated with a first portion of the webpage, receiving, at a network interface a request for the webpage from the user interface, transmitting, by the network interface, the webpage and a software module including an instruction associated with the bypass block navigation function in response to the request, causing display, at the user interface, the webpage at a starting location, receiving, from the user interface, a keystroke associated with the bypass block navigation function, and causing display, at the user interface, of the first portion of the webpage in response to the instruction associated with the bypass block navigation function.

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.

Abstract: In a method for applying deterministic logic to select resources for resource genomes in a genetic algorithm, a logic engine identifies resources associated with an objective and an overall task population to be completed by one or more of the identified resources. The logic engine then selects a deterministic logical framework from one or more deterministic logical frameworks based on the objective. Following the selection of a deterministic logical framework, the logic engine selects one or more resources from the one or more identified resources based on the selected deterministic logical framework. The logic engine compiles the one or more selected resources into a resource genome, assigns one or more tasks from the task population to the one or more selected resources, and sends instructions to the one or more selected resources to execute the one or more tasks. The logic engine determines a value score for the resource genome.

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.

Abstract: In a method for applying deterministic logic to select resources for resource genomes in a genetic algorithm, a logic engine identifies resources associated with an objective and an overall task population to be completed by one or more of the identified resources. The logic engine then selects a deterministic logical framework from one or more deterministic logical frameworks based on the objective. Following the selection of a deterministic logical framework, the logic engine selects one or more resources from the one or more identified resources based on the selected deterministic logical framework. The logic engine compiles the one or more selected resources into a resource genome, assigns one or more tasks from the task population to the one or more selected resources, and sends instructions to the one or more selected resources to execute the one or more tasks. The logic engine determines a value score for the resource genome.

Abstract: In a method for determining seasonality in inventory demand, an inventory analytics engine uses a frequency of reordering to identify a micro-season for an inventory item. The inventory analytics engine further uses linear regression analysis to identify a variable most closely associated with the micro-season. The inventory analytics engine adjusts a reordering schedule to accommodate an expected demand for the inventory item.

Abstract: In a method for applying deterministic logic to select resources for resource genomes in a genetic algorithm, a logic engine identifies resources associated with an objective and an overall task population to be completed by one or more of the identified resources. The logic engine then selects a deterministic logical framework from one or more deterministic logical frameworks based on the objective. Following the selection of a deterministic logical framework, the logic engine selects one or more resources from the one or more identified resources based on the selected deterministic logical framework. The logic engine compiles the one or more selected resources into a resource genome, assigns one or more tasks from the task population to the one or more selected resources, and sends instructions to the one or more selected resources to execute the one or more tasks. The logic engine determines a value score for the resource genome.

Abstract: In an approach to handling a plurality of heuristics to establish safety stock limits using genetic algorithms, a product genome evaluation engine generates a first product genome from a population of products, identifies a safety stock objective, and identifies multiple products associated with the safety stock objective. The product genome evaluation assigns a product heuristic byte that defines product heuristics that can be applied to each product of the multiple products to the first product genome, assigns a product heuristic that defines at least one inventory policy from the product heuristics to the each product at random, and applies each product heuristic associated with the each product. Based on the overall effect of the product heuristics on each product, the product genome evaluation engine establishes safety stock limits for each product and determines whether the safety stock limits achieve the safety stock objective.

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.

Abstract: A computer-implemented method for providing a design representation of a software system includes storing in a memory a dataset representative of a plurality of software code elements of the software system, wherein storing the dataset includes incorporating design data into the dataset, the design data being representative of respective design properties regarding the software code elements. A dataset query including a reference to an item of the dataset is received. The dataset query is implemented, with a processing unit in communication with the memory, over the dataset. Implementing the dataset query includes defining a subset of the dataset based on the reference to the item. A user interface rendered via an output device in communication with the processing unit user interface includes a view of the design representation of the software system in accordance with the defined subset of the dataset.