Abstract: Example methods, apparatus and articles of manufacture to determine work placement preferences are disclosed. A disclosed example method includes receiving, via a processor, a request for a resource to fill an open position within an organization, the request including a primary resource group for the open position, determining with the processor resource group preferences of resource groups using a structure of the organization that includes functional relationships between the resource groups, calculating weights for the resource group preferences based on respective organizational distances in the structure of the respective resource groups from the primary resource group, determining a target resource group with a greatest calculated weight for a resource group preference from among the resource groups that have at least one available resource, and selecting a resource within the target resource group to fill the open position.

Abstract: A workforce capacity and capability at an entity is optimized. Values for parameters related to the workforce capacity and capability at the entity are received. A workforce capacity and capability model is used to generate values for decision variables related to the workforce capacity and capability at the entity, based on the values for the parameters that have been received. The workforce capacity and capability model models uncertainty associated with a workforce.

Abstract: An embodiment of a method of scheduling computer processing begins with a first step of receiving job properties for a plurality of jobs to be processed in a multi-processor computing environment. At least some of the jobs each comprise a plurality of stages, one or more tasks for each stage, and precedence constraints among the stages. The method continues with a second step of determining a schedule for processing at least a subset of the plurality of jobs on processors within the multi-processor computing environment from a solution of a mathematical program that provides a near maximal completion reward. The schedule comprises a sequence of tasks for each processor. In a third step, the computer processing jobs are processed on the processors according to the sequence of tasks for each processor.

Abstract: Methods, apparatus, and computer readable media with executable instructions stored thereon for optimizing resource skill compliance are provided. Example methods of the present disclosure can include identifying jobs, skills corresponding to the jobs, and resources corresponding to the skills. A job skill level is associated with each skill of each job, and a resource skill level is associated with each skill of each resource. An individual skill compliance is determined by computer for each skill of each resource with respect to each skill of each job based on the resource skill level relative to the job skill level. A resource skill compliance is determined by computer for each resource with respect to each job as a total of the individual skill level compliances for each skill corresponding to each job, and resources are assigned by a computer to jobs in a manner that optimizes a total of the resource skill compliances for those resources assigned to jobs.

Abstract: A report indicating a user-reported probability of a successful outcome is received. A behavior and information model is estimated based on the report. The behavior and information model includes a behavior model component having a bias parameter and a consistency parameter. The behavior and information model includes an information model component having a first user-believed probability of a successful outcome and a second user-believed probability of a successful outcome. The behavior and information model is used to yield a model-determined probability of a successful outcome that more accurately reflects a probability of a successful outcome than the user-reported probability of a successful outcome does.

Abstract: A resource is scored based on a matching of the resource to a job. Job attributes for the job and resource attributes for the resource are identified. A hierarchy of the job attributes that includes dependencies between the job attributes is determined. A score for the resource is determined using the hierarchy of the job attributes and the resource attributes. The score represents a matching between the resource and the job.

Abstract: One embodiment is a method that receives historical data of suppliers and applies, to the historical data, a mathematical optimization system that includes a set of mathematical equations and inequalities that express capabilities and capacities of the suppliers. The mathematical optimization system includes an objective function that minimizes a number of the suppliers to perform third-party labor services for an enterprise. The method selects a sub-set of the suppliers to perform the third-party labor services for the enterprise.

Abstract: A method of workforce plan evaluation includes receiving (110) a resource data (440) that includes data relating to employee resources, and data relating to a set of opportunities representing demand for employee resources; and receiving a workforce plan (450) associated with the resource data. A realization model configured to simulate effects of supply and demand uncertainty on the resource data is established (130) and executed (140) over a given time period (240) of the planning window (230) for which the workforce plan (450) is generated. The resource data (440) is transformed (150) according to the predictions of the resource model, and metrics configured to assess demand fulfillment and/or resource utilization achieved by the workforce plan under the realization model are computed (160).

Abstract: A method is disclosed for evaluating the performance of a website. An agent interacts with the website using a behavior model of an exemplary website customer. The agent interacts with the website according to the behavior model and gathers website performance data related to the interaction. The performance data is compared to a utility function for the behavior model. A rating is assigned to the website based on the comparison, and the rating is made available to potential website customers seeking information related to the website's performance.

Abstract: Example embodiments disclosed herein relate to a mechanism for optimizing a resource matching model. In particular, a mechanism is provided to access, in a resource matching system, input data for a mixed integer programming (MIP) model, which may include resource data describing resources and demand data describing corresponding demand instances. Mechanisms are also provided to convert the MIP model to a binary integer programming (BIP) model by redefining the input data to unary data and to map the BIP model to a bipartite graph using the unary data. The resulting bipartite graph may include a number of nodes including a first set corresponding to the resources and a second set corresponding to the demand instances, and a number of edges corresponding to decision variables of the BIP model, each edge representing a potential allocation of a resource in the first set to a demand instance in the second set.

Abstract: A method of employee resource planning includes consolidating and defining a demand for employee resources needed to accomplish a potential project in terms of a plurality of jobs and a supply of employee resources in terms of a plurality of employees, each job and each employee having a plurality of job attributes uniformly categorized into indices. Employees in the supply are mapped to corresponding jobs in the demand according to the job attributes.

Abstract: Systems, methods, and other embodiments associated with workforce planning. One example method includes receiving a project data associated with a project opportunity. The project data includes a probability data. The probability data describes the likelihood of a project opportunity being selected. The project data includes a workforce demand. The example method may also include establishing an objective function that is based, at least in part, on the project data, and a set of constraints. The objective function concerns a workforce allocation plan. The example method may also include minimizing a workforce cost by solving the objective function in light of the set of constraints. Solving the objective function includes optimizing a tradeoff between labor utilization and project demand fulfillment. The example method may also include providing the workforce allocation plan.

Abstract: In at least some embodiments, a method comprises computing an initial schedule of jobs to be run on a computing system using a mathematical program and monitoring the computing system. The method also comprises, based on the monitoring, determining, using the mathematical program used to compute the initial schedule, whether the initial schedule should be re-computed.

Abstract: Embodiments of the present invention relate to a system and method for allocating resources to applications. One embodiment of the present invention may include identifying a plurality of applications and a plurality of application components, and determining available resources of a networked computing system for potentially storing the applications and application components. Additionally, the embodiment may include determining required resources for each application component, assigning a subset of available resources for each application component based on the required resources of the application component and the available resources. The embodiment may also include associating the application components with the subsets of available resources and storing more than one application component of the plurality of application components on a single available resource.

Abstract: Embodiments of the present invention relate to a system and method for assigning resources to an application component. One embodiment of the present invention includes identifying an application component, and determining available resources of a networked computing system for running the application component. Additionally, the embodiment includes determining required resources for the application component, assigning a subset of available resources for the application component taking into account the required resources of the application component, the available resources, and one or more operator policies. These operator policies generally include preferences of operators, administrators, users, or the like, and could also include business rules. Additionally, the embodiment includes associating the application component with the subset of available resources.

Abstract: A method, system, and apparatus is disclosed for allocating resources to applications. Available resources of a networked computing system may be determined. For each application, required resources of the application may be determined. An assigned subset of the available resources for each application is determined as a function of the required resources of the application and the available resources. The function reduces communication delays between resources of the subset of the available resources in conformance with bandwidth capacity requirements of the application and in conformance with network bandwidth limitations. The applications may then be associated with the assigned subsets of resources.

Abstract: A method for resource allocation in a data center is disclosed which employs modeling techniques that enable resource allocation in large-scale data centers. A method for resource allocation according to the present techniques includes providing a model for resource allocation which is adapted to a resource topology of a data center and an architecture of an application to be deployed in the data center. A set of resources in the data center are allocated to the application using the model in response to a set of resource requirements of the application and a set of attributes of the resources in the data center such that communication delay among the resources allocated to the application is minimized. Techniques are disclosed for decomposing the model into a set of more tractable programming models which may be solved using available mathematical programming solvers.

Abstract: A configuration method includes selecting a design configuration, and modeling the solution configuration having a failure event for the selected solution configuration to determine an amount of down time associated with the failure event. A first penalty cost function is applied the amount of down time to determine a first penalty cost associated with the failure event of the selected solution configuration.

Abstract: An embodiment of a method of designing a storage system begins with a step of assigning decision variables to a range of candidate storage systems. Each of the decision variables identifies a single candidate storage system. The method concludes with a step of employing the decision variables in a mathematical program to determine which of the candidate storage systems meets availability and reliability criteria and incurs a near minimal projected cost.

Abstract: A system and method employing an allocation process for determining the number of server machines at each tier of a multiple tiered server system. The allocation process evaluates the number of server machines at each tier sufficient to achieve an average response time of a transaction request to be processed by the server system in response to changes in the average number of transaction requests. The allocation process also identifies shadow pricing enabling analysis of the cost associated with incremental changes in the average response time or other critical system resources.