Abstract: A method for optimizing allocations in a workforce allocation plan begins when resource data is received. The resource data includes data that relates to employee resources and to demand for the employee resources. A first set of employee resource pairings is established using the user's resource preferences to assigning the employee resources to the demand for the employee resources. A second set of employee resource pairings is established using predetermined resource preferences to assign the employee resources to the demand for the employee resources. The second set of employee resource pairings includes all employee resource pairings that belong to an optimal solution. The first and second sets of employee resource pairings are compared to provide the workforce allocation plan having the optimal solution with the maximum number of the user's resource preferences to optimize allocations in the workforce allocation plan.

Abstract: A method for cost entity matching can include constructing a first graph including a number of first entities and a number of second entities, and assigning a number of first cost values to each of a number of first edges connecting one of the number of first entities to one of the number of second entities in the first graph; adding a second graph to the first graph, wherein the second graph includes the number of first entities and the number of second entities, and assigning a second cost value to each of a number of second edges connecting one of the number of first entities in the first graph to the corresponding first entity in the second graph; and, matching each one of the number of second entities to one of the number of first entities based on the number of first cost values and the second cost values.

Abstract: Systems, methods, and computer-readable and executable instructions are provided for awarding a group-targeted promotion. Awarding a group-targeted promotion can include defining a first condition for a customer to join a list of promotion-eligible customers and defining a second condition for a number of groups of customers from the list to qualify for a group-targeted promotion. Awarding a group-targeted promotion can include awarding the group-targeted promotion to a group among the number of groups that complies with the second condition, wherein the method is performed by a computing device.

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: 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: Systems, methods, and machine readable and executable instructions are provided for target level setting. Target level setting may include receiving a plurality of objectives for an organization that includes a central unit and a plurality of regional units, transmitting a respective regional target level for each of the plurality of objectives assigned to each of the plurality of regional units, prompting the central unit for an approval of an increase to a first regional target level for a first of the plurality of regional units and a decrease to a second regional target level for a second of the plurality of regional units, increasing the first regional target level for the first of the plurality of regional units, and decreasing the second regional target level for the second of the plurality of regional units.

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: 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.